-
Grant agreement n°633080
MACC-III Deliverable D_46.1.1 and 50.1.1
CHIMERE regional forecasting system and performances Dossier #6
June-Jul.-Aug. 2014 Sept.-Oct.-Nov. 2014
Date: 02/2015 Lead Beneficiary: MF-CNRM (#23) Nature: R
Dissemination level: PU
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Work-package 46 and 50 (ENS, model forecasts and verification)
Deliverable D46.1.1 and D50.1.1 Title Dossiers documenting regional
forecasting systems and
their performances & Verification part of model dossiers
commenting in particular skill scores by trimestrial periods
Nature R Dissemination PU Lead Beneficiary MF-CNRM (#23) Date
02/2015 Status Final version Authors Laurence Rouïl, Bertrand
Bessagnet, Anthony Ung,
Frédérik Meleux (INERIS,#17) Matthias Beekmann, Gilles Foret,
Adriana Coman, Benjamin Gaubert (CNRS-LISA, #10) Laurent Menut
(CNRS-LMD, #10)
Approved by Virginie Marécal Contact
[email protected]
[In case the deliverable is not a report: provide a description
of it inside this box.]
This document has been produced in the context of the MACC-III
project (Monitoring Atmospheric Composition and Climate). The
research leading to these results has received funding from the
European Community's Horizon 2020 Programme under grant agreement
n° 633080. All information in this document is provided "as is" and
no guarantee or warranty is given that the information is fit for
any particular purpose. The user thereof uses the information at
its sole risk and liability. For the avoidance of all doubts, the
European Commission has no liability in respect of this document,
which is merely representing the authors view.
2 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Executive Summary / Abstract The MACC III (Modelling Atmospheric
Composition and Climate, www.copernicus-atmosphere.eu) project is
establishing the core global and regional atmospheric environmental
service delivered as a component of Europe's GMES/Copernicus
(Global Monitoring for Environment and Security) initiative. The
regional forecasting service provides daily 4-days forecasts of the
main air quality species (ozone, NO2, and PM10) from 7
state-of-the-art atmospheric chemistry models and from the median
ensemble calculated from the 7 model forecasts. This report
documents the CHIMERE regional forecasting system and its
statistical performances against in-situ surface observations for
quarter#21 (June, July, August 2014) and quarter#22 (September,
October, November 2014). The Chimere version remains the same for
these periods to the one used one year before. During quarter #21,
the operationalization of the forecast and analyses chains required
significant changes. The forecast files are now produced earlier
than previously: the first 48-hours of the forecast are now
produced before 7h UTC everyday. To achieve this target, the
ensemble production chain has been split into slots of 24-hours
terms. These important changes may explain why the availability
statistics of some individual models were degraded during quarters
#21 and #22. Verification is achieved using the up-to-date methods
described in the MACC-II dossiers covering quarters #15 and #16. In
this dossier, the dataset of surface observations used for
verification is collected from the EEA/EIONET NRT database.
Experience from the MACC-II/OBS subproject (Deliverable D_16.3) has
shown that this pre-operational service is enough reliable and it
offers a better geographical coverage than the previous database
that was used for MACC-II verification. As for the past three
years, the verification statistics are based on the use of only
representative sites selected from the objective classification
proposed by Joly and Peuch (Atmos. Env. 2012). During these two
quarters, the CHIMERE performances compared to the Ensemble and
also to the results of CHIMERE for previous periods show different
features. For ozone, the scores decreased with a different time
profile. It is very complex to understand why as the version of
CHIMERE does not change over the last few years. The station data
from the EEA database which are partly different from the set used
previously may explain part of the changes. The scores for NO2 are
stable. And a significant improvement for PM10 is shown which could
be due to the insertion of the near real time boundary conditions
for aerosols in the forecasting chain compared to the set-up of
last year.
3 / 42
http://www.copernicus-atmosphere.eu/http://www.copernicus-atmosphere.eu/
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Table of Contents
1. CHIMERE facts sheet
..............................................................................................................
6
1.1. Products portfolio (at the end of quarter#22)
............................................................ 6
1.2. Performance statistics
.....................................................................................................
6
1.3. Availability statistics
........................................................................................................
6
1.4 Assimilation and forecasts system: synthesis of main
characteristics ............................ 7
2. Evolutions in the CHIMERE
suite............................................................................................
9
3. CHIMERE background information
......................................................................................
10
3.1 Forward model
...............................................................................................................
10
3.1.1 Model Geometry
.........................................................................................................
10
3.1.2 Forcings and boundary values
.....................................................................................
11
3.1.2.1 Meteorology
.............................................................................................................
11
3.1.2.2 Chemistry
.................................................................................................................
11
3.1.2.3 Landuse
....................................................................................................................
11
3.1.2.4 Surface emissions
.....................................................................................................
11
3.1.3 Dynamical core
............................................................................................................
12
3.1.4 Physical Parametisations
.............................................................................................
12
3.1.4.1 Turbulence
...............................................................................................................
12
3.1.4.2 Deposition
................................................................................................................
12
3.1.5 Chemistry
....................................................................................................................
12
3.2 Assimilation system
........................................................................................................
12
3.2.1 Optimal Interpolation
..................................................................................................
13
3.2.2 Ensemble Kalman Filter (EnKF)
...................................................................................
13
3.2.2.1 Model version coupled to the EnKF
.........................................................................
13
3.2.2.2 Assimilation method: Ensemble Kalman Filter
........................................................ 14
3.2.2.3 Covariance Modelling
...............................................................................................
16
3.3 Developments achieved and plans
................................................................................
18
ANNEX A: Verification report for quarter#21
..........................................................................
23
4 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
ANNEX B: Verification report for quarter#22
..........................................................................
33
5 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
1. CHIMERE facts sheet
1.1. Products portfolio (at the end of quarter#22)
Name Description Freq. Available for users at
Species Time span
FRC Forecast at surface,50m,250m, 500m,1000m,2000m, 3000m, 5000m
above ground
Daily 9:00 UTC
O3, NO2, CO, SO2,PM2.5, PM10, NO, NH3, NMVOC, PANs, Birch pollen
at surface during season
0-96h, hourly
ANA Analysis at the surface
Daily 11:00 UTC for the day before
O3 and PM10 0-24h of the day before, hourly
1.2. Performance statistics See annexes.
1.3. Availability statistics The statistics below describe the
ratio of days for which the MATCH model outputs were available on
time to be included in the ensemble fields (analyses and forecasts)
that are computed at Météo-France. The following labels are used
referring to the reason of the problem causing unavailability: (P)
if the failure comes from the individual regional model production
chain (T) if this is related to a failure of the data transmission
from the partners to Météo-France central site (C) if this is a
failure due to the central processing at Météo-France (MF) Quarter
21 (June, July, August 2014). The ratio of days on which CHIMERE
forecasts and analyses were provided on time is:
Terms Analyses 0-96h frc Availability 53 % 79 %
To be included in the ensemble, the model outputs should be
received at Météo-France before: 14:00 UTC for the analysis and
11:00 UTC for the forecast. CHIMERE forecasts were missing from 18
to 23(P), on 29(P) June 2014, on 1(P), 5(P), 7(P), 11(P), 19(T),
22(P) and from 28(P) to 31(P) July 2014, from 05 to 06(P), on 16(P)
and 22(P) August 2014.
6 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE analyses were missing on 3(P), from 17 to 24(P), from 29
to 30(P) June 2014, on 5(P), 7(P), 8(P), 11(P), 19(P) and from 24
to 31(P) July 2014, on 01(P), from 5 to 8(P), from 13 to 25(P)
August 2014. During quarter #21, we had some difficulties to
produce due to instability on our computing system which makes our
computing time too long to meet with the daily deadline of
production. Quarter 22 (September, October, November 2014) : the
ratio of days on which CHIMERE forecasts and analyses were provided
in time is: Terms Analyses 0-24h frc 25-48h frc 49-72h frc 73-96h
frc Availability 54 % 68 % 65 % 46 % 24 %
To be included in the ensemble, the model outputs should be
received at Météo-France before: 11:30 UTC for the analysis, 5:00
UTC for the 0-24h forecast, 6:00 UTC for the 25-48h forecast, 6:45
UTC for the 49-72h forecast and 7:30 UTC for the 73-96h forecast.
Availability of CHIMERE forecasts was incomplete on 3(P), 7(P),
from 10 to 18(P), from 20 to 30(P) September 2014, from 1 to 5(P),
from 7 to 12(P), from 14 to 31(P) October 2014, from 1 to 5(P),
from 10 to 14(P), from 16(P) to 19(P), on 21(P), from 23 to 24(P),
from 26 to 30(P) November 2014. CHIMERE analyses were missing on
3(P), 7(P), from 10 to 18(P), on 21(P), from 24 to 30(P) September
2014, from 3 to 5(P), on 7(P), from 9 to 12 (P), from 14 to 16(P),
on 21(P), from 24 to 29 (P) October 2014, and from 3 to 4 (T), on
21 (P) and on 28(P) November 2014. During quarter #22, we had some
difficulties to produce due to instability on our computing system
which makes our computing time too long to meet with the daily
deadline of production.
1.4 Assimilation and forecasts system: synthesis of main
characteristics Assimilation and Forecast System Horizontal
resolution 0.1° Vertical resolution Variable, 8 levels from the
surface up to
500 hPa Gas phase chemistry MELCHIOR2, comprising 44 species and
120
reactions (Derognat, 2003) Heterogeneous chemistry Aerosol size
distribution 8 bins from 10 nm to 40 μm Inorganic aerosols Primary
particle material, nitrate, sulphate,
ammonium Secondary organic aerosols Biogenic, anthropogenic
Aqueous phase chemistry Dry deposition/sedimentation Classical
resistance approach
7 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Mineral dust Dusts are considered Sea Salt Inert sea salt
Boundary values Values provided by MACC-GRG Initial values 24h
forecast from the day before Anthropogenic emissions MACC-TNO
inventory Biogenic emissions Forecast System Meteorological driver
00:00 UTC operational IFS forecast from
the day before Assimilation System (not yet activated for daily
operations) Assimilation method Optimal Interpolation, Ensemble
Kalman
filter Observations Surface ozone (rural) and PM10 Frequency of
assimilation Every hour over the day before Meteorological driver
00:00 UTC operational IFS forecast for the
day before
8 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
2. Evolutions in the CHIMERE suite 2014/04: additional levels
and species in the daily grib2 file provision 2014/01: New grib2
file 2013/07/20: Boundary conditions from NRT global forecasts (Gas
+ aer) – or MACCII climatology 2013/03/01: implementation of the
birch pollen module 2012/10/10: Extension of the MACCII domain in
CHIMERE and calculation of D+3. 2012/06/20: Bug fixed in the
emission processing which can have severe effects on the model
performances. 2012/01/01: New version of CHIMERE running with
horizontal resolution of 0.1°. 2010/01/06: The system is running
robustly at INERIS. 2009/12/01: Operational delivery of the new
CHIMERE version runs for the MACC AQ forecasts with discontinuities
during December. 2009/10/01: Interruption of the CHIMERE forecast
daily delivery due to the system migration from LISA (ECMWF) to
INERIS. 2009/06/01: start of MACC pre-operational forecasts.
9 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
3. CHIMERE background information
3.1 Forward model The CHIMERE multi-scale model is primarily
designed to produce daily forecasts of ozone, aerosols and other
pollutants and make long-term simulations for emission control
scenarios. CHIMERE runs over a range of spatial scale from the
regional scale (several thousand kilometres) to the urban scale
(100-200 Km) with resolutions from 1-2 Km to 100 Km. On this
server, documentation and source codes are proposed for the
complete multi-scale model. However most data are valid only for
Europe and should be revisited for applications on other
continents. CHIMERE proposes many different options for simulations
which make it also a powerful research tool for testing
parameterizations The chemical mechanism (MELCHIOR) is adapted from
the original EMEP mechanism. Photolytic rates are attenuated using
liquid water or relative humidity Boundary layer turbulence is
represented as a diffusion (Troen and Mahrt, 1986, BLM) Vertical
wind is diagnosed through a bottom-up mass balance scheme. Dry
deposition is as in Wesely (1989). Wet deposition is included Six
aerosol sizes represented as bins in the model. Aerosol
thermodynamic equilibrium is achieved using the ISORROPIA model.
Several aqueous-phase reactions considered Secondary organic
aerosols formation considered Advection is performed by the PPM
(Piecewise Parabolic Method) 3d order scheme for slow species. The
numerical time solver is the TWOSTEP method. Its use is relatively
simple provided input data is correctly provided. It can run with
several vertical resolutions, and with a wide range of complexity.
It can run with several chemical mechanisms, simplified or more
complete, with or without aerosols. CHIMERE is a parallel model
that has been tested on machines ranging from desktop PCs running
the GNU/Linux operating system, to massively parallel
supercomputers (HPCD at ECMWF). CHIMERE is a French national CNRS
tool meaning that source code and documentation are freely
available on a dedicated web site, training courses are organized
twice a year for users. More than 120 users, from 30 institutes,
are registered on the model e-mail list.
3.1.1 Model Geometry CHIMERE is an eulerian deterministic model,
using variable resolution in time and space (for cartesian grids).
The model uses any number of vertical layers, described in hybrid
sigma-p coordinates. The model runs over the GEMS-MACC domain with
a 0.1° resolution and 8 vertical levels extending from the surface
up to 500 hPa.
10 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
3.1.2 Forcings and boundary values The model is off-line and has
to be forced for meteorology and boundary conditions. Two
interfaces has been develop to connect CHIMERE with the chemical
boundary conditions deliver by the Mozart global forecasts. The
first one dedicated to gaseous species is active in MACC since the
boundary conditions are provided operationally. The second one for
aerosols (Sea salt, dust, black carbon...) is not used yet waiting
for the daily provision of boundary conditions for particulate
matter.
3.1.2.1 Meteorology CHIMERE can use many meteorological models
and interfaces are provided for the following models: MM5, WRF,
IFS/ECMWF. For most studies done with CHIMERE, the MM5 model was
used forced by the National Centers for Environmental Prediction
(NCEP) global meteorological data. MM5 was configured with the PBL
option MRF (Option 5), based on the Troen and Mahrt (1986)
parameterization (the most consistent with the CHIMERE mixing
formulation). The Schultz (Option 8) microphysics parameterization
has also been tested with CHIMERE and is recommended. Within, MACC,
CHIMERE is directly forced by the IFS forecasts from the daily
operational products delivered at 00 UTC.
3.1.2.2 Chemistry Boundary conditions can be either "external"
or given by a coarse resolution CHIMERE simulation. In case of
"external" forcing, the model is provided with several databases:
The LMDz-INCA model [Hauglustaine et al., 2005] for gas-phase
chemical species. The global aerosol model GOCART for mineral
aerosols [Chin et al., 2004] or the CHIMERE-DUST outputs. The
3-hourly GRG global forecast is used to provide boundary conditions
for a set of pollutants in MACC.
3.1.2.3 Landuse The proposed domain interface is based on the
Global Land Cover Facility (GLCF:
http://glcf.umiacs.umd.edu/data/landcover 1kmx1km resolution
database from the University of Maryland, following the methodology
of Hansen et al. (2000, J. Remote Sensing).
3.1.2.4 Surface emissions The model provides an interface
combining several emissions sources such as EMEP (Yearly totals),
IER (Time variations), TNO (Aerosol emissions), UK Dept of
Environment (VOC speciation, Passant, 2002). The MACC emissions
(TNO) are used in CHIMERE for MACC.
11 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
3.1.3 Dynamical core Three advection schemes are implemented:
The Parabolic Piecewise Method (PPM, a three-order horizontal
scheme, after Colella and Woodward, 1984), the Godunov scheme (Van
Leer, 1979) and the simple upwind first-order scheme.
3.1.4 Physical Parametisations
3.1.4.1 Turbulence Vertical turbulent mixing takes place only in
the boundary-layer. The formulation uses K-diffusion following the
parameterization of [Troen and Mahrt, 1986], without
counter-gradient term.
3.1.4.2 Deposition Dry deposition is considered for model gas
species i and is parameterized as a downward flux F(d,i)= -v(d,i)
c(i) out of the lowest model layer with c(i) being the
concentration of species i. The deposition velocity is, as
commonly, described through a resistance analogy [Wesely, 1989].
The wet deposition follows the scheme proposed by [Loosmore,
2004]
3.1.5 Chemistry
CHIMERE offers the option to include different gas phase
chemical mechanisms. The original, complete scheme [Lattuati,
1997], hereafter called MELCHIOR1, describes more than 300
reactions of 80 gaseous species.
The hydrocarbon degradation is fairly similar to the EMEP gas
phase mechanism [Simpson, 1992]. Adaptations are made in particular
for low NOx conditions and NOx-nitrate chemistry. All rate
constants are updated according to [Atkinson, 1997] and [De More,
1997]. Heterogeneous formation of HONO from deposition of NO2 on
wet surfaces is now considered, using the formulation of [Aumont,
2003]. In order to reduce the computing time a reduced mechanism
with 44 species and about 120 reactions is derived from MELCHIOR
[Derognat, 2003], following the concept of chemical operators
[Carter, 1990]. This reduced mechanism is called MELCHIOR2
hereafter.
MACC CHIMERE version consists in the baseline gas-phase version
with MELCHIOR2 chemistry, together with a sectional aerosol module.
This module accounts for 7 species (primary particle material,
nitrate, sulfate, ammonium, biogenic secondary organic aerosol SOA,
anthropogenic SOA and water). Potentially, Chloride et Sodium can
be included (high computing time). In its initial version the
module uses 6 bins from 10 nm to 40 μm. Now the module moves to 8
bins from 10 nm to 10 μm.
3.2 Assimilation system
12 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
During a first stage, waiting for the development of the
ensemble kalman filter, we will use an optimal interpolation method
to assimilate daily concentration values for correcting the raw
forecasts of CHIMERE. This method has been widely evaluated in the
Prev’Air system for ozone and PM10.
3.2.1 Optimal Interpolation
The analysis method is designed to assess, as accurately as
possible, near–real time surface concentration fields of ozone and
PM10. CHIMERE analysis is carried out over France and since summer
2009 over Europe. The observations retrieved in near–real time are
used in combination with D-1 daily maxima for ozone, D-1 daily mean
for PM10 and D-1 hourly values for both. We use one of the methods
proposed by Blond et al [2003], based on the kriging of the
differences between simulated and observed values, often called
innovations in meteorology. Kriging methods have the advantage of
providing spatial interpolations that necessitate few assumptions
and give robust results. Few assumptions are needed in kriging
methods; a sensitivity analysis on the kriging parameters has been
performed, enabling to select the most appropriate parameters. The
choice of the measurement sites is a crucial stage in the analysis
procedure: the monitoring stations selected must deliver
concentrations representative of the gridded concentrations. Rural
stations are selected in priority, then suburban stations and urban
stations, provided that the influence of local sources of pollution
and local meteorology is minor. At a given location s, the analyzed
concentration is calculated from the following equation:
( ) ( ) ( ) ( ) ( )( )kbkp
kkkba sZsYswsZsZ −+= ∑
=0
1
where Za(s) refers to the analyzed concentration at site s;
Zb(s) refers to the corresponding simulated value; Yo(sk) is the
measured concentration at site sk and wk(sk) are the weights
derived from the kriging constraints (see Blond et al. [2003] for
more details about the method). Innovations Yo(sk) - Zb(sk) are
estimated at each monitoring site sk. The kriging method used here
is ‘‘exact’’: at the measurement sites, the analyzed concentration
is equal to the observed concentration.
3.2.2 Ensemble Kalman Filter (EnKF) The ensemble Kalman Filter
is now coupled to the CHIMERE model. It allows to assimilate
(separately at the moment) ozone measurements from ground based
stations and from the IASI instrument on board the METOP
platform.
3.2.2.1 Model version coupled to the EnKF The CHIMERE model
(http://www.lmd.polytechnique.fr/chimere/) is a regional
Chemistry-Transport Model. It is used operationally since 2006 for
Air Quality monitoring and forecasting by the French platform
PREVAIR (http://www.prevair.org/fr/index.php) for main
13 / 42
http://www.lmd.polytechnique.fr/chimere/http://www.prevair.org/fr/index.php
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
atmospheric pollutants (O3, NO2, PM10). In the framework of
MACC, forecast and analysis are produced using this model either
for R-ENS either for R-EVA. The version of the model used in R-EDA
is slightly different and the aim of the work made in this
sub-project is to develop and evaluate an Ensemble Kalman Filter
(EnKF) coupled with this model to eventually replace the current OI
algorithm used operationally in R-ENS and R-EVA. Nevertheless, most
of model features remain unchanged. We use pre-processors to
compute various forcing needed by the core model. The IFS 12-hourly
analysis and complementary 3-hourly forecast are used as off-line
meteorological forcing. A meteorological module (diagmet) is used
to diagnose additional meteorological variables needed such as
boundary layer height. The anthropogenic emission pre-processor
ingests TNO emissions (Visschedijk et al., 2007) to determine
hourly emission fluxes available for the model. Biogenic emissions
are calculated using the MEGAN module (Guenther et al, 2006) fed by
hourly meteorological variables. We also use 3-hourly chemical
forcing at top and boundaries of the domain from the IFS-MOZART
system (Fleming et al., 2009). In this version, the horizontal
resolution to cover the GEMS domain is 0.5°x0.5°. 20 hybrid
(σ,p)vertical levels are used on the vertical covering the
1000hPa-200hPa range. The model is coupled with a sequential
EnKF.
3.2.2.2 Assimilation method: Ensemble Kalman Filter
An advanced sequential data assimilation method (EnKF) has been
set-up for the purpose of 3D data assimilation. We use ensembles,
generated by using Monte Carlo methods, to calculate spatially and
temporally varying forecast-error covariances for the purpose of
performing data assimilation.
In mathematical terms, the general data assimilation problem is
defined by the computation of the probability density function
(PDF) of the model solution, conditioned on the measured
observations, (i.e. following the Bayes theorem, we have to
estimate a posterior PDF). This PDF is usually represented using
statistical moments or an ensemble of model states and searching
for estimators like mean, mode or maximum likelihood. In the case
of the EnKF, since the size of the ensemble is limited, it is
difficult to obtain a very accurate representation of the PDF in
high dimensional problems (Evensen, 2007). We restrict ourselves to
finding a good estimate for the mean of the PDF. In the case of the
EnKF (with Gaussian hypothesis for the errors), the ensemble mean
and covariance describe the PDF of the assimilated fields because
Gaussian PDFs are fully determined by their mean and variance; thus
the solution becomes computationally feasible.
The analysis equation which allows us to up-date each ensemble
member is written as:
( ) ( )fiTfeTfefiai HdRHHPHP Ψ−++Ψ=Ψ −1 (1) where fiΨ represents
an ensemble member i (model state) (“f” stands for forecast, “a”
for analysis), d is the vector of observations available at the
time of analysis, H represents the linear version of the
observation operator which permits the projection from the model
space onto the observation space, feP is the forecast covariance
error matrix, R is the observation covariance error matrix and
( ) 1−+= RHHPHPK TfeTfee (2)
14 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
is known as the Kalman gain matrix, where “T” refers to the
transposed matrix. The “best estimate” is calculated as a mean over
the ensemble members using the formula (with N the ensemble
size):
∑=
Ψ=ΨN
i
ai
a
N 11 (3)
And the analysed covariance error matrix Pa as the covariance
over the ensemble:
( )( )TaaiN
i
aai
ae N
P Ψ−ΨΨ−Ψ−
= ∑=11
1 (4)
The same formula is used for the forecast covariance error
matrix ( feP ) using f
iΨ insteadaiΨ
(and fΨ instead aΨ ).
The initial ensemble ( ) Nifi ,1=Ψ , whose mean is the current
state estimate, is updated in the analysis step (Eq. 1) taking into
account all knowledge about the error statistics (in the model, feP
, and in the measurements, R). The key of this method is the
transformation of the forecast ensemble into an analysis ensemble (
) Niai ,1=Ψ with appropriate statistics. They are two ways to treat
uncertainty in observations: one consists in adding perturbations
to them according to the observational error in order to obtain N
vectors of measurements ( ) Niid ,1= (N is the ensemble size). In
this case, in Equation 1, we will use id instead of d to update
each member of the ensemble (details in Burgers et al. 1998). In
this manner, we avoid the loss of the ensemble spread after
assimilation. A second alternative is to use a Square Root Filter
formulation (Maybeck, 1979). This formulation avoids the loss of
positive definiteness of the error covariance matrices. It was
demonstrated that the elimination of the sampling error associated
with the perturbed observations makes the EnSRF (Ensemble Square
Root Filter) more accurate than the EnKF for the same ensemble size
(Whitaker and Hamill, 2002, Sakov and Oke, 2008). This is the
reason for selecting square root formulation in our study (we use
the same formulas and notations as in Evensen, 2004).
Knowing that usually the ensemble size tested is up to one
hundred members, and that the number of observations associated
with AQ is at least ten times larger, it seems, when considering
the size of the matrix involved in inversion, that we have to solve
an ill-conditioned problem. It may be extremely difficult to
accurately evaluate the inverse of a matrix when the largest
eigenvalue may be many orders of magnitude larger than its smallest
eigenvalue. An ensemble with a limited number of members cannot
estimate accurately the background error across the entire state
space due to spurious error correlations; therefore it is better to
restrict the new information provided by the measurement to a local
neighbourhood. Thus, “covariance localisation” has become a very
widely used technique to filter out the spurious long-range
correlations, and increase the rank of the background covariance
matrix. In the Houtekamer and Mitchell (1998) formulation, the size
and ill-conditioning problems are simultaneously solved by using a
cutoff radius beyond which covariances between variables are
assumed to be zero. This is the choice made in our assimilation
system.
To assimilate satellite data, we have to be able to project the
vectors from the model space onto the observation space, calculate
the innovations (differences between the
15 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
observations and the simulated fields projected in the
observational space) and reproject this information onto the
forecast model space. All these operations are achieved by
constructing H, the observation operator. The formula used is:
( ) ( )fifi LASH ψψ ⋅⋅= (5) Making observations and simulated
fields comparable first implies performing a vertical interpolation
L (in order to have the same number of vertical layers for the
model and for the IASI retrieval, one layer for each km up to 12
km). The second operation is a convolution by the averaging kernel
A. As already mentioned, the averaging kernel matrix provides the
information which, if properly applied to a particular in situ
profile data, transforms that profile in order to have the same
resolution and a priori dependence as the IASI retrievals (see the
equation below from Rodgers, 2000):
( ) ( ) amama xAIAxxxAxx −+=−+=ˆ (6) where xm represents the
model simulated profile, xa is the a priori profile (here issued
from the McPeters climatology), I is the identity matrix and A is
the averaging kernel; in this way we transform the model profile
into a pseudo retrieved profile. Note that in the assimilation
case, adding the a priori profile is not needed (Rodgers, 2000)
because the a priori was removed from the IASI columns before,
therefore only the first term in Equation 6 ( )mAx is required. The
last step for constructing H is the integration on the vertical
(S), up to 6 km, in order to obtain a scalar value corresponding to
the column value.
For surface observations, the H allows a simple “interpolation”
of model’s values in the grid cell corresponding to the station’s
geographical localisation.
3.2.2.3 Covariance Modelling
At the moment, both types of observations are assimilated
separately with two different configurations of the system.
Distinction is made in the following for both systems.
Nevertheless, the basic idea remain the same, at least for
Background error covariance matrix: the ensemble formulation is
used to describe time varying error of the model. As described
below, the way of building the ensemble is different with the
nature of observation used. It should be noted that this aspects
will be changed for further versions of the system for which we are
currently building a unified version.
Background Error Covariance Matrix
Satellite observations (ozone) (Coman et al, 2011)
There are several methods to set-up an ensemble, but no unified
theory has been developed yet, at least for chemistry-transport
simulations (Galmarini et al., 2004). Ensembles can be derived from
a single model while perturbing model parameters (Beekmann and
Derognat, 2003), numerical and physical parameterisations (Mallet
and Sportisse, 2006) or more basically just perturbing a set of
initial conditions. In this case, the initial ensemble was created
by applying 3-dimensional “pseudo-random” perturbations to a
reference run. These perturbations were taken from a Gaussian
distribution with zero mean, unitary variance and a Gaussian
spatial covariance with a fixed decorrelation length (Evensen,
16 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
1994) in order to obtain a 2-dimensional field. Given two such
pseudo-random fields for two distinct layers, a new couple of
fields vertically correlated with a specific covariance between
layers can be generated (Eq. A13-A14 from Evensen, 1994). This
procedure is applied for the 20 correlated perturbations fields
corresponding to the model layers. The 3-dimensional perturbation
field obtained after this procedure is characterised by a
decorrelation length fixed at 200 km in the horizontal and at 1 km
in the vertical, following the comparisons between a model
reference run and ground-based/MOZAIC observations, presented in
Boynard et al. (2010). The amplitude of perturbations was fixed at
10% of the simulated ozone concentrations in each grid cell.
Perturbations were applied each 3 hours during the spin-up period
of 24 h, and then during the whole assimilation period. During the
one day forecast periods, between two analyses, these perturbations
accumulated to give a dispersion of the ensemble from the mean
varying between 17% and 25%. This is consistent with the model
error statistics established by comparison with surface and free
tropospheric ozone observations (Honoré et al., 2008). No temporal
correlation was used in this configuration (i.e. white noise was
assumed).
Surface observations (ozone)
First, we follow the same methodology as for the satellite. An
additive inflation was added hourly for the ozone field of each
ensemble members, in particular a normally distributed perturbation
(characterized by a zero mean and a decorrelation length fixed at
200 km). On the vertical, the correlation of this additive noise is
one in the boundary layer and no perturbations above. In addition,
a physically sound ensemble was created by perturbing model
parameters and input fields which explain most of ozone variability
(Hanea et al., 2004). The main parameters that affect the ozone
variability were fixed following the sensitivity tests presented in
Boynard et al. (2010). Perturbations of the parameters are
Log-normally distributed with fixed standard deviation; spatial
correlations are also taken into account (mostly characteristic of
the synoptic scale). Consequently, although we obtain a better
spatial structure of the error field, the dispersion of the
ensemble associated to the parameter perturbations broadly
underestimates the error.
Finally, the perturbation of the ozone state largely dominates.
In the initial set-up, perturbations of the ozone field were
prescribed using time-varying perturbations during the day (between
15 and 25%). Now, we are using the Desroziers diagnostic
(Desroziers et al. 2005, Schwinger et al. 2011) of the previous day
to adjust the standard deviation of the perturbations (cf. Equation
5). In this case, the ensemble spread is weaker, but the temporal
structure is better giving similar results than previous system
(with a more stable algorithm).
(5)
The term yjo denote ozone measurements made at station j over p
stations, yja and yjb are the corresponding values in observation
space of the analyzed and background state respectively.
Observation Error Covariance Matrix
∑ =
−
−= p
j
b
j
o
j
b
j
a
jb yyyyp 1
1σ
17 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Satellite observations (ozone) (Coman et al, 2011)
The errors in the observations were regrouped
(representativeness and instrumental error) in the R matrix, whose
diagonal is filled with the results obtained during the inversion
procedure (Eremenko et al., 2008). We consider that there is no
error correlation between different satellite observations used
simultaneously in the assimilation (R is diagonal). This is
certainly a simplification, but the degree of horizontal error
correlation is unknown. However, only a limited set of satellite
observations is used within the adopted localisation procedure (see
below). We see that in our case, the observation error of the 0–6
km ozone column is about 16% on the average over the pixels
available for the whole month. In the retrieval procedure the
diagnosed error is not temporally correlated, thus we do not
consider such a correlation in our system.
We apply a local analysis in order to avoid spurious
correlations in the background ensemble, which are introduced by
the perturbation method for finite ensemble sizes, and which do not
have any geophysical reality. The basic idea of localisation is to
perform the analysis at a given grid point using the observations
within a local region centred at that point. The radius of this
region was fixed at 200 km, corresponding to the decorrelation
length in the horizontal perturbations applied (following Boynard
et al., 2010). The maximum number of observations to be assimilated
was limited to 30 pixels. This parameter was chosen after
sensitivity tests. No vertical localisation was applied.
Surface observations (ozone)
The observation error covariance matrix is build as the sum of
the measurement error (fairly reliable in the case of ozone: ~1-2%)
and of the representativeness error. This representativeness error
is more difficult to estimate because it should express spatial
representativeness of a single measurement which is depending of
the local environment of the station (land-use, emissions) and the
meteorology (boundary layer height, synoptic and local winds).
These parameters are difficult to characterise and often varying in
time (at diurnal and/or seasonal scales).
The methodology used in this case was to define a representative
set of stations (compared to the model resolution, i.e 0.5°x0.5°)
following the classification of Fleming et al. (2005). Thus, we
obtain clusters of stations namely MOU (for mountain or at least
remote sites), RUR (for rural sites) and PUR (U1) (for peri-urban
sites) that are eligible for data assimilation. The classification
allows an objective clustering; i.e based on the measurements
themselves more precisely on the mean diurnal cycle observed at
each station. Moreover, we wanted to derive representativeness
errors for assimilation purposes. Considering that those
representativeness errors are unbiased, we have calculated the
standard deviation between different sites contained in the same
model grid cell and we found values about 4-5 ppb that are
consistent with the results obtained by Fleming et al. 2004. Thus,
assuming that observations errors are uncorrelated, diagonals terms
of the observation error covariance matrix are finally set to
25ppb².
3.3 Developments achieved and plans
18 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Future works will concern the development of the fire emission
module and the implementation of a new CHIMERE version which is
less time consuming.
19 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
References Beekmann M., and Derognat C.: Monte Carlo uncertainty
analysis of a regional-scale transport
chemistry model constrained by measurements from the atmospheric
pollution over the Paris area (ESQUIF) campaign, J. Geophys. Res.,
108(D17), 8559, doi:10.1029/2003JD003391, 2003.
Bessagnet B., L. Menut, G. Aymoz, H. Chepfer and R. Vautard,
Modelling dust emissions and transport within Europe: the Ukraine
March 2007 event, J. Geophys. Res., 113, D15202,
doi:10.1029/2007JD009541, 2008.
Bessagnet B., L.Menut, G.Curci, A.Hodzic, B.Guillaume,
C.Liousse, S.Moukhtar, B.Pun,
C.Seigneur, M.Schulz, Regional modeling of carbonaceous aerosols
over Europe - Focus on Secondary Organic Aerosols, J. Atmos. Chem.,
in press, 2009.
Boynard, A., Beekmann, M., Foret, G., Ung, A., Szopa, S.,
Schmechtig, C. and Coman, A.: Assessment of regional ozone model
uncertainty with a modelling ensemble using an explicit error
representation, Atm. Env., 45, 784-793, 2011.
Burgers, G., Van Leeuwen, P.J. and Evensen, G.: Analysis Scheme
in the Ensemble Kalman Filter, Mon. Weather Rev., 126, 1719-1724,
1998.
Coman, A., Foret, G., Beekmann, M., Eremenko, M., Dufour, G.,
Gaubert, B., Ung, A., Schmechtig, C., Flaud, J.-M., and G.
Bergametti, Assimilation of IASI partial tropospheric columns with
an Ensemble Kalman Filter over Europe, 26943-26997, 11, ACPD,
2011.
de Meij A., Gzella, A., Cuvelier, C., Thunis, P., Bessagnet, B.,
Vinuesa, J.F., Menut, L., Kelder H.,
The impact of MM5 and WRF meteorology over complex terrain on
CHIMERE model calculations, Atmos. Chem. Phys. , in press,
2009.
Desroziers, G., L. Berre, B. Chapnik and P. Poli, Diagnosis of
observation, background and analysis-error statistics in
observation space, Q. J. R. Meteorol. Soc., 131, 3385–3396 doi:
10.1256/qj.05.108, 2005.
Eremenko, M., Dufour, G., Foret, G., Keim, C., Orphal, J.,
Beekmann, M., Bergametti, G., and Flaud, J.-M.: Tropospheric ozone
distributions over Europe during the heat wave in July 2007
observed from infrared nadir spectra recorded by IASI, Geophys.
Res. Lett., 35, L18805,doi:10.1029/2008GL034803, 2008.
20 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Evensen, G., Sequential data assimilation with a nonlinear
quasigeostrophic model using Monte Carlo methods to forecast error
statistics, J. Geophys. Res., 99 (C5), 10143-10162, 1994.
Evensen, G.: Sampling strategies and square root analysis
schemes for the EnKF, Ocean Dynamics, 54, 539-560, DOI
10.1007/s10236-004-0099-2, 2004.
Evensen, G.: Data assimilation: The Ensemble Kalman Filter,
Springer-Verlag Berlin Heidelberg, 2007.
Flemming, J., van Loon, M., Stern, R., 2004. Data assimilation
for CTM based on optimum interpolation and KALMAN filter. In:
Borrego, C., Incecik, S. (Eds.), Air Pollution Modeling and its
Application, vol. XVI. Kluwer Academic/Plenum Publishers, New
York.
Flemming, J., A., Inness, H., Flentje, V., Huijnen, P., Moinat,
M. G., Schultz, and O. Stein, Coupling global chemistry transport
models to ECMWF’s integrated forecast system, Geosci. Model Dev.,
2, 253-265, 2009.
Galmarini S., Bianconib R., Klug W. et al.: Ensemble dispersion
forecasting – part I: Concept, approach and indicators, Atmos.
Environ., 38, 4607– 4617, 2004.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I.
and Geron, C.: Estimates of
global terrestrial isoprene emissions using MEGAN (Model of
Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys.,
6, 3181-3210, 2006.
Hanea, R., Velders, G. and Heemink, Data assimilation of ground
level ozone in Europe with a
kalman filter and chemistry transport model, J. Geophys. Res.,
109, 5183-5198, 2004. Honoré C., L. Rouil, R. Vautard, M. Beekmann,
B. Bessagnet, A. Dufour, C. Elichegaray , J.-M.
Flaud, L. Malherbe, F. Meleux, L. Menut, D. Martin, A. Peuch,
V.-H. Peuch, N. Poisson, Predictability of European air quality:
the assessment of three years of operational forecasts and analyses
by the PREV'AIR system, J. Geophys. Res., 113, D04301, doi:
10.1029/2007JD008761, 2008.
Houtekamer, P.L. and Mitchell, H.L.: Data assimilation using an
Ensemble Kalman Filter technique, Mon. Weather Rev.,126, 796-811,
1998.
Mallet, V., and Sportisse B.: Uncertainty in a
chemistry-transport model due to physical parameterizations and
numerical approximations: An ensemble approach applied to ozone
modeling, J. Geophys. Res., 111, D01302, doi:10.1029/2005JD006149,
2006.
21 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Maybeck, P.: Stochastic models, estimation, and control,
Academic Press, London, 1979.
Rodgers, C.D.: Inverse Methods for Atmospheric Sounding: Theory
and Practice, World Scientific, Series on Atmospheric, Oceanic and
Planetary Physics, 2, Hackensack, N. J., 2000.
Rouil L., C. Honore, R. Vautard, M. Beekmann, B. Bessagnet, L.
Malherbe, F. Meleux, A. Dufour,
C. Elichegaray, J.-M. Flaud, L. Menut, D. Martin, A. Peuch,
V.-H. Peuch, N. Poisson, PREV'AIR : an operational forecasting and
mapping system for air quality in Europe, Bull. Am. Meteor. Soc.,
doi: 10.1175/2008BAMS2390.1, 2009.
Sakov, P. and Oke, P.R.: Implications of the form of the
ensemble transformation in the ensemble square root filters,
Monthly Weather Review, 136, 1042-1053, 2008.
Schwinger, J., and H. Elbern, Chemical state estimation for the
middle atmosphere by four-dimensional variational data
assimilation: A posteriori validation of error statistics in
observation space, J. Geophys. Res., 115, doi:10.1029/2009JD013115,
2010.
Szopa S., G. Foret, L. Menut, A. Cozic, Impact of large scale
circulation on European summer
surface ozone: consequences for modeling, Atmospheric
Environment, 43(6), Pages 1189-1195,
doi:10.1016/j.atmosenv.2008.10.039, 2009.
Valari M. and L. Menut, Does increase in air quality models
resolution bring surface ozone concentrations closer to reality?,
J. Atmos. Ocean. Tech., doi: 10.1175/2008JTECH A1123.1, 2008.
Vivanco M. G., Palomino I., Vautard R., Bessagnet R., Martin F.,
Menut L., Jimenez S., Multi-year assessment of photochemical air
quality simulation over Spain, Env. Mod. and Software,
doi:10.1016/j.envsoft.2008.05.004, 2008.
Visschedijk, A.J.H., Zandveld, P.Y.J., and Denier van der Gon,
H.A.C.A., High resolution gridded European database for the EU
Integrate Project GEMS, TNO-report 2007-A-R0233/B.
Whitaker, J.S. and Hamill, T.M.: Ensemble Data assimilation
without perturbed observations, Mon. Weather Rev., 130, 1913-1924,
2002.
22 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
ANNEX A: Verification report for quarter#21 This verification
report covers the period June/July/August 2014. The CHIMERE skill
scores are successively presented for three pollutants: ozone, NO2
and PM10. The skill is shown for the entire forecast horizon from 0
to 96h (hourly values), allowing to evaluate the entire diurnal
cycle and the evolution of performance from day 0 to day 3. Since
November 2012, the forecast has been extended to 96h range and over
a larger European domain (25°W-45°E, 30°N-70°N) than previously.
According to VAL subproject recommendations (D85.2), the five
following statistical indicators are used for model skill
evaluation: mean bias, root-mean square error, modified normalized
mean bias, fractional gross error and the correlation. Quarter #21
is the first one for which the observation dataset for verification
is collected from the Up-To-Date (UTD) data stream of the European
Environmental Agency (EEA). In MACC, verifications were done
against all available Near-Real-Time (NRT) data. Since MACC-II,
verifications have been performed against selected data among the
NRT dataset from the different countries in Europe. Data provision
to MACC relied on ad hoc bilateral agreement with Environment
Agencies in 14 different countries. In MACC, D-INSITU work showed a
considerable number of differences, the EEA dataset reporting more
sites for ozone and less for the other species. Efforts were done
to merge the two data sources, by helping EEA to get access to data
in the countries with which GEMS-MACC has been in contact and which
do not provide all their NRT data to EEA. In MACC-II, the work to
merge EEA and data currently received in NRT advanced well. There
were some technical issues on data formats and availability times
of the EEA dataset, that have been mostly solved, in collaboration
with OBS subproject (see MACC-II/D-16.3).
The observations from EEA/EIONET are downloaded and are stored
in an operational database at MF-CNRM. As in MACC-II, the
observations are selected in order to take into account the
typology of sites, follows the work that has been carried out in
MACC [Joly and Peuch, 2012] to build an objective classification of
sites, based on the past measurements available in Airbase (EEA)
(see MACC D_R-ENS_5.1 for more details). This objective approach is
necessary because there is no uniform and reliable metadata
currently for all regions and countries, which have all different
approaches to this documentation. Verification is thus restricted
to the sites that have a sufficient spatial representativeness with
respect to the model resolution (10-20 km). The statistical
approach using only representative sites -according to the
objective classification- is clearly the way forward (as it does
not also thin too much the NRT data available), leading to a
general significant improvement of the overall skill scores (see
MACC-II D_102.1_1/D106.1_1 for more details). Filtering stations on
the EEA/EIONET NRT data leads to a mean numbers of: ~500 sites for
ozone, ~400 sites for NO2, ~100 sites for SO2, ~10 sites for CO,
~300 sites for PM10. These numbers are lower than the number of
stations available for previous years, but we expect an improvement
of data collection and delivery from the EEA in the coming months.
Note also that stations from Germany were missing during the whole
quarter #21, decreasing even more these numbers. Checking the daily
observation datasets revealed some inconsistencies that needed to
be addressed in relation with EEA, such as undesirable zero
concentration values and unrealistic time series at some stations.
Some ad hoc treatments of the observations have been introduced at
MF-CNRM.
23 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Figure 1 :coverage of surface observations used for
verification, collected from the EEA, and
after filtering.
The usage of the observation dataset is twofold: for
verification of the forecasts and also for assimilation in the
regional models. To be used for data assimilation, downloading the
observations at 7h UTC is a reasonable compromise between the
amount of data and the desired early time of production of the
analyses. It will give the possibility to produce soon the regional
analyses earlier, around 11h UTC. However, the number of
observations at the end of the day decreases rapidly (Figure 2),
due to the fact that some countries do not report observations to
the EEA during the night. For forecast verification, observations
are thus downloaded later, at 23h UTC, which leads to a more
homogeneous distribution over the day (Figure 2). Similarly to
forecast verification, MF-CNRM plans to set up procedures for
verification of the NRT analyses. To get prepared, MF-CNRM has set
up a sorting of observations, so that some stations are not
distributed for assimilation, but kept for future verification
scores of NRT analyses.
24 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Figure 2 : mean number of observations available per hour of the
day, during November 2014, for assimilation, for forecast
verification and for future analysis verification.
Joly, M. and V.-H. Peuch, 2012: Objective Classification of air
quality monitoring sites over Europe, Atmos. Env., 47, 111-123.
25 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE: ozone skill scores against data from representative
sites, period #21 (June, July, August 2014)
CHIMERE has a higher bias than the Ensemble. The lowest bias
occurs when ozone concentration are high in the mid-afternoon.
Diurnal cycles are similar for both and quite stable for all
time-lags. Compared to previous year, the results are quite worst
during the morning with 6 µg/m3 more. This high inter-annual
variability is difficult to understand as well as the change in the
temporal profile. Otherwise, the minimum bias is slightly better
than last year.
CHIMERE RMSE is higher than the ensemble one. Minimum RMSE
occurred during daytime when ozone concentration is maximum. The
difference between CHI and ENS looks stable from one day to
another, even if the score becomes worse. The scores seem to be
worse than last year with 2 µg/m3 more and the gap between CHI and
ENS RMSE bigger. The time profile looks also a bit different.
As for the mean bias, the maximum correlation occurs during the
mid-afternoon. The CHIMERE correlation is close to ENS correlation.
The score is far better than last year. Once again. such
improvement is difficult to explain.
26 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
The time profile of the modified mean bias of CHIMERE is very
similar to the Ensemble one. A slight difference occurs during
daytime when values are lowest. More or less the same values as
last year .
The time profile of the fractional gross error of CHIMERE is
very similar to the Ensemble one. A slight difference occurs during
daytime when values are lowest. More or less the same values as
last year.
27 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE: NO2 skill scores against data from representative
sites, period #21 (June, July, August 2014)
The CHIMERE mean bias is positive during nighttimes and becomes
negatives at daytime. The daily variability is higher in CHIMERE
than in the Ensemble. Very close to the results of last year with a
temporal profile slightly different.
The diurnal cycle is a bit different between CHIMERE and the
Ensemble. CHIMERE RMSE is higher than the Ensemble during the
evening peak (traffic rush) and both show similar rmse during the
morning traffic rush. Compared to last year, the rmse of CHIMERE
evening peak decreases by 3-4 µg/m3.
The correlations are similar for both. High values at nighttimes
and low values in daytimes. Lower values than last year.
28 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
The negative modified mean bias of CHIMERE is lower than the
Ensemble with a similar diurnal cycle. The underestimation of
CHIMERE appears less important than for the Ensemble. Same
conclusions as last year.
The over/under estimation is higher in the Ensemble than in
Chimere during daytime. Similar to last year.
29 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE: PM10 skill scores against data from representative
sites, period #21 (June, July, August 2014)
CHIMERE bias depicts a lower underestimation than the ENS one.
The diurnal cycle is similar for both. The improvement of CHIMERE
(especially compared to ENS) is significantly important compared to
last year. This could be due to the insertion of BC conditions for
aerosols in the CHIMERE forecasts.
CHIMERE and ENSEMBLE have similar RMSE with the same temporal
profile which shows a high variability and stability regarding from
one day to another. As for the bias, the improvement comparing to
last year is significant.
The Chimere correlation is lower than that of the Ensemble with
the same diurnal cycle. The correlations are very low and lower
than last year.
30 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE underestimation is lower than the ENSEMBLE one.
The fractionnal gross error of CHIMERE is lower than the
Ensemble with a similar time profile. It’s the opposite of last
year.
31 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Analysis of CHIMERE performances for quarter#21 The comparison
of CHIMERE against observations for this quarter (June 2014 to
August 2014) shows.
1) About ozone: The Chimere scores decrease compared to last
year without possible explanation as the model version is still the
same and the input data as well. The temporal profile has changed
also and the temporal variability is quite high. The daily
variability is stable from one day to another. The scores
modifications could therefore be due to different meteorological
conditions and to the use of a different dataset for verification
(EEA database versus files acquired country by country). The future
version of CHIMERE would provide improvement of process for ozone
like the on-line calculation of photolysis rates and more efficient
model settings for chemistry.
2) About NO2 The scores are more or less stable compared to last
year with performances showing a large intra-day variability,
higher than the Ensemble. The profile is similar from one day to
another.
3) PM10 Compared to the previous year, the score show
significant improvements regarding the bias and RMSE but not
correlation. This could be due to the insertion of boundary
conditions for aerosols which should be responsible for a decrease
of the bias. Further possible improvements would be to activate the
computation of the secondary inorganic aerosol online.
32 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
ANNEX B: Verification report for quarter#22 This verification
report covers the period September/October/November 2014. The
CHIMERE skill scores are successively presented for three
pollutants: ozone, NO2 and PM10. The skill is shown for the entire
forecast horizon from 0 to 96h (hourly values), allowing to
evaluate the entire diurnal cycle and the evolution of performance
from day 0 to day 3. Since November 2012, the forecast has been
extended to 96h range and over a larger European domain (25°W-45°E,
30°N-70°N) than previously. According to VAL subproject
recommendations (D85.2), the five following statistical indicators
are used for model skill evaluation: mean bias, root-mean square
error, modified normalized mean bias, fractional gross error and
the correlation. Quarter #21 was the first one for which the
observation dataset for verification is collected from the
Up-To-Date (UTD) data stream of the European Environmental Agency
(EEA). In MACC, verifications were done against all available
Near-Real-Time (NRT) data. Since MACC-II, verifications have been
performed against selected data among the NRT dataset from the
different countries in Europe. Data provision to MACC relied on ad
hoc bilateral agreement with Environment Agencies in 14 different
countries. In MACC, D-INSITU work showed a considerable number of
differences, the EEA dataset reporting more sites for ozone and
less for the other species. Efforts were done to merge the two data
sources, by helping EEA to get access to data in the countries with
which GEMS-MACC has been in contact and which do not provide all
their NRT data to EEA. In MACC-II, the work to merge EEA and data
currently received in NRT advanced well. There were some technical
issues on data formats and availability times of the EEA dataset,
that have been mostly solved, in collaboration with OBS subproject
(see MACC-II/D-16.3).
The observations from EEA/EIONET are downloaded and are stored
in an operational database at MF-CNRM. As in MACC-II, the
observations are selected in order to take into account the
typology of sites, follows the work that has been carried out in
MACC [Joly and Peuch, 2012] to build an objective classification of
sites, based on the past measurements available in Airbase (EEA)
(see MACC D_R-ENS_5.1 for more details). This objective approach is
necessary because there is no uniform and reliable metadata
currently for all regions and countries, which have all different
approaches to this documentation. Verification is thus restricted
to the sites that have a sufficient spatial representativeness with
respect to the model resolution (10-20 km). The statistical
approach using only representative sites -according to the
objective classification- is clearly the way forward (as it does
not also thin too much the NRT data available), leading to a
general significant improvement of the overall skill scores (see
MACC-II D_102.1_1/D106.1_1 for more details). Filtering stations on
the EEA/EIONET NRT data leads to a mean numbers of: ~500 sites for
ozone, ~400 sites for NO2, ~100 sites for SO2, ~10 sites for CO,
~300 sites for PM10. These numbers are lower than the number of
stations available for previous years, but we expect an improvement
of data collection and delivery from the EEA in the coming months.
Note also that stations from Germany were missing during September
and October, decreasing even more these numbers. Checking the daily
observation datasets revealed some inconsistencies that needed to
be addressed in relation with EEA, such as undesirable zero
concentration values and unrealistic time series at some stations.
Some ad hoc treatments of the observations have been introduced at
MF-CNRM.
33 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Figure 1 :coverage of surface observations used for
verification, collected from the EEA, and
after filtering.
The usage of the observation dataset is twofold: for
verification of the forecasts and also for assimilation in the
regional models. To be used for data assimilation, downloading the
observations at 7h UTC for the day before is a reasonable
compromise between the amount of data and the desired early time of
production of the analyses. It will give the possibility to produce
soon the regional analyses earlier, around 11h UTC. However, the
number of observations at the end of the day decreases rapidly
(Figure 2), due to the fact that some countries do not report
observations to the EEA during the night. For forecast
verification, observations are thus downloaded later, at 23h UTC,
which leads to a more homogeneous distribution over the day (Figure
2). Similarly to forecast verification, MF-CNRM plans to set up
procedures for verification of the NRT analyses. To get prepared,
MF-CNRM has set up a sorting of observations, so that some stations
are not distributed for assimilation, but kept for future
verification scores of NRT analyses.
34 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Figure 2 : mean number of observations available per hour of the
day, during November 2014, for assimilation, for forecast
verification and for future analysis verification.
Joly, M. and V.-H. Peuch, 2012: Objective Classification of air
quality monitoring sites over Europe, Atmos. Env., 47, 111-123.
35 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE: ozone skill scores against data from representative
sites, period #22 (September, October, November 2014)
CHIMERE has a higher bias than the Ensemble. The lowest bias
occurs when ozone concentration are high in the mid-afternoon.
Diurnal cycles are similar for both and quite stable for all
time-lags. Compared to previous year, the results are worst during
the morning with 2 µg/m3 more. Otherwise, the minimum bias is
similar to last year. The CHIMERE bias is also similar to the
previous period.
CHIMERE RMSE is higher than the ensemble one. Minimum RMSE
occurred during daytime when ozone concentration is maximum. The
difference between CHI and ENS looks stable from one day to
another. The scores seem to be worse than last year with 2 µg/m3
more and the gap between CHI and ENS RMSE bigger. The time profile
looks also a bit different with less intra-day variability now.
The CHIMERE correlation is close to ENS correlation. The score
is better than last year and close to the one of the previous
period.
36 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
The time profile of the modified mean bias of CHIMERE is very
similar to the Ensemble one. A difference occurs during daytime
when values are lowest. A slight improvement compared to last
year.
The time profile of the fractional gross error of CHIMERE is
very similar to the Ensemble one. A slight difference occurs during
daytime when values are lowest. A slight improvement compared to
last year.
37 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE: NO2 skill scores against data from representative
sites, period #22 (September, October, November 2014)
The CHIMERE mean bias is negative except for the morning hours
corresponding to the traffic rush. The daily variability is a bit
higher in CHIMERE than in the Ensemble. Very close to the results
of last year with a temporal profile slightly different.
CHIMERE RMSE is very close to the ENSEMBLE RMSE with the same
time profile. A small gap between both happens at lowest values.
Compared to last year, the score is similar
The correlations are similar for both. High values at nighttimes
and low values in daytimes. The variability is high. Same values as
last year.
38 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
The negative modified mean bias of CHIMERE is similar to the
Ensemble with a similar diurnal cycle. A small period occurs every
for CHIMERE with over-estimation during morning rush traffic hours.
The underestimation of CHIMERE appears less important than for the
Ensemble. Similar to last year.
Almost the same score for the Ensemble and CHIMERE except during
nighttime.
39 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
CHIMERE: PM10 skill scores against data from representative
sites, period #22 (September, October, November 2014)
CHIMERE bias depicts a lower underestimation than the ENS one.
The diurnal cycle is similar for both. Compared to last year,
CHIMERE is now continuously underestimated (opposite to last year
with a permanent overestimation). The difference between ENS and
CHI is lower.
CHIMERE and ENSEMBLE have similar RMSE with the same temporal
profile which shows a high variability and stability regarding from
one day to another. The improvement comparing to last year is very
significant.
The Chimere correlation is lower than that of the Ensemble with
the same diurnal cycle. The chimere performance is slightly better
than last year.
40 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
The negative modified mean bias of CHIMERE is lower than the
Ensemble with a similar diurnal cycle. The underestimation of
CHIMERE appears less important than for the Ensemble. Same
conclusions as last year.
The CHIMERE fractional gross error displays close values to ENS
but decrease earlier in the mid-afternoon.
41 / 42
-
File:
MACCIII_ENS_DEL_D_46.1.1_D_50.1.1_JJA-SON2014_CHIMERE_Dossier6.docx.doc/.pdf
Analysis of CHIMERE performances for quarter#22 The comparison
of CHIMERE against observations for this quarter (Sep 2014 to Nov
2014) shows.
1) About ozone: The Chimere scores decrease compared to last
year without possible explanation as the model version is still the
same and the input data as well. The temporal profile of scores has
changed also and the temporal variability is quite high. The daily
variability is stable from one day to another. The performances
looks similar to the one we have got for the previous period. The
future version of CHIMERE would provide improvement of process for
ozone like the on-line calculation of photolysis rates and more
efficient model settings for chemistry.
2) About NO2 The scores are more or less stable compared to last
year and also last period with performances showing a large
intra-day variability, higher than the Ensemble. The profile is
similar from one day to another.
3) PM10 Compared to previous year, the score show significant
improvements regarding the bias and RMSE but not correlation. The
characteristics are similar to the previous period. This could be
due to the insertion of boundary conditions for aerosols which
should be responsible for a decrease of the bias. Further possible
improvements would be to activate the computation of the secondary
inorganic aerosol online.
42 / 42
1. CHIMERE facts sheet1.1. Products portfolio (at the end of
quarter#22)1.2. Performance statistics1.3. Availability
statistics1.4 Assimilation and forecasts system: synthesis of main
characteristics
2. Evolutions in the CHIMERE suite3. CHIMERE background
information3.1 Forward model3.1.1 Model Geometry3.1.2 Forcings and
boundary values3.1.2.1 Meteorology3.1.2.2 Chemistry3.1.2.3
Landuse3.1.2.4 Surface emissions3.1.3 Dynamical core3.1.4 Physical
Parametisations3.1.4.1 Turbulence3.1.4.2 Deposition3.1.5
Chemistry3.2 Assimilation system3.2.1 Optimal Interpolation3.2.2
Ensemble Kalman Filter (EnKF)3.2.2.1 Model version coupled to the
EnKF3.2.2.2 Assimilation method: Ensemble Kalman Filter3.2.2.3
Covariance Modelling3.3 Developments achieved and plans
ANNEX A: Verification report for quarter#21ANNEX B: Verification
report for quarter#22