regional production quarterly report on the daily analyses ......regional production quarterly...

Regional productionQuarterly report on the daily analyses and forecasts activities, and verification of the MOCAGE performances

December 2015 – January 2016 – February

2016

Issued by: METEO-FRANCE

Date: 04/05/2016

REF.:

CAMS50_2015SC1_D50.3.2.MOCAGE-2016Q1_201605

CAMS50_2015SC1_D50.3.4.MOCAGE-2016Q1_201605

CAMS50_2015SC1_D50.5.1.MOCAGE-2016Q1_201605

Copernicus Atmosphere Monitoring Service

Qr. report on daily analyses and forecasts activities, verification of the MOCAGE performances |

This document has been produced in the context of the Copernicus Atmosphere Monitoring

Service (CAMS). The activities leading to these results have been contracted by the

European Centre for Medium-Range Weather Forecasts, operator of CAMS on behalf of the

European Union (Delegation Agreement signed on 11/11/2014). 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 and the European Centre

for Medium-Range Weather Forecasts has no liability in respect of this document, which is

merely representing the authors view.



Quarterly report on the dailyanalyses and forecasts activities, and verification ofthe MOCAGE performances

December 2015 – January 2016 – February 2016

METEO-FRANCE (M. Pithon, M. Plu, J. Parmentier,

J. Arteta, S. Guidotti, N. Asencio, N. Assar)

Date: 04/05/2016

REF.:

CAMS50_2015SC1_D50.3.2.MOCAGE.02_201605

CAMS50_2015SC1_D50.3.4.MOCAGE.02_201605

CAMS50_2015SC1_D50.5.1.MOCAGE.02_201605


Contents:


1. Executive Summary.........................................................................................42. The MOCAGE model (METEO-FRANCE)................................................................5Product portfolio..................................................................................................5Availability statistics.............................................................................................5

Use for observations for data assimilation...............................................................73. Verification report..........................................................................................10Verification of NRT forecasts................................................................................10Verification of NRT analyses................................................................................15


1. Executive Summary

The Copernicus Atmosphere Monitoring Service (CAMS, www.copernicus-atmosphere.eu) is establishing the core global and regional atmospheric environmental

service delivered as a component of Europe's Copernicus programme. The regionalforecasting service provides daily 4-days forecasts of the main air quality species andanalyses of the day before, from 7 state-of-the-art atmospheric chemistry models andfrom the median ensemble calculated from the 7 model forecasts. The regional service

also provides posteriori reanalyses using the latest validated observation datasetavailable for assimilation.

This report covers the deliverables related to Near Real Time Production (NRT) for

MOCAGE: D50.3.2.MOCAGE-2016Q1, D50.3.4.MOCAGE-2016Q1, D50.5.1.MOCAGE-2016Q1, for the quarter December 2015 – January 2016 – February 2016. Verificationis done against in-situ surface observations; they are described in the report D50.1.2-2016Q1, that will be delivered shortly. The verification of analyses is done against non

assimilated observations.

During this quarter, the reliability of the MOCAGE production was good, with a 99%availability for the analyses and for the first-day forecasts, and decreasing down to

89% for the fourth-day forecast. The failures were due to the acquisition of surfacemeteorological forcings and to the provision of pollen input data, that have beensecured since then.

During this quarter, the performance of MOCAGE forecasts and analyses was similar asduring past winter, except for ozone, for which a bug had been inducing an increasingbias. This bug is under correction. NO2 scores have improved since past winter. PM10

performances remain poor as long as the formation of secondary aerosols is not taken

into account. Development work is on-going for MOCAGE on this aspect. Theassimilation of ozone and NO2 surface observations improves significantly thecorresponding surface concentration fields. The assimilation of PM10 surface observationis under development.

Qr. report on daily analyses and forecasts activities, verification of the MOCAGE performances | 4


2. The MOCAGE model (METEO-FRANCE)

Product portfolio

Name Forecast Analysis

Description Forecast at surface, 50m,

250m, 500m, 1000m, 2000m,

3000m, 5000m above ground

Analysis at the surface

Available for users at 0:30 UTC 10:30 UTC for the day before

Species O3, NO2, CO, SO2, PM2.5, PM10,

NO, NH3, NMVOC, PANs,

Birch pollen at surface during

season

O3, NO2

Time span 0-96h, hourly 0-24h for the day before,

hourly

Availability statistics

The statistics below describe the ratio of days for which the MOCAGE model outputs

were available on time to be included in the ENSEMBLE fields (analyses and forecasts)that are computed at METEO-FRANCE. They are based on the following schedule forthe provision at METEO-FRANCE of:

- forecasts data before: 05:30 UTC for D0-D1 (up to 48h), 07:30 UTC for D2-D3 (from49h to 96h) - analyses data: before 11:00 UTC

These schedules have been set to meet the IT requirements for ENSEMBLE products(no later than 8 UTC for 0-48h, 10 UTC for 49-96h and 12 UTC for analyses).

Indicators:

Availability_MOCAGE_Forecast

Quarterly basis

D0: 99%

D1: 96%

D2: 90%

D3: 89%

Availability _MOCAGE_Analysis

Quarterly basis

D: 99%



During this quarter, the following issues have been encountered by the MOCAGE

production system:

Date Problem description

(origin, effects)

Impact

on production

02/12/2015 Analysis not provided

17/12/2015 Step 95 missing (timeout) D3 too late for ENSEMBLE

From 09/01/2016 to

11/01/2016

Problem of acquisition of met.

surface forcing due to HPC

switch

D2-D3 forecasts not provided

on Jan 9th

D1-D2-D3 forecasts not

provided on Jan 11th

From 14/01/2016 to

15/01/2016

Issue regarding met. forcing

acquisition


30/01/2016 Issue regarding met. forcing

acquisition


From 27/02/2016 to

29/02/2016

Issue related with treatment

of pollen input data


on Feb 27th

D1-D2-D3 forecasts not

provided on Feb 28th

D0-D1-D2-D3 not provided on

Feb 29th

Comments:

The issue related to pollen input data non available has been definitively corrected and

production of other species has been secured against potential pollen forecast failure.

Acquisition of surface meteorological data, which was the cause of 3 main failures, willgain a full operational status at the end of Phase 0.



Use for observations for data assimilation

Day

Use of observation for MOCAGE

December

O3 NO2 NO SO2 CO PM10 PM2.5

1 8,687 9,281

2 8,658 9,509

3 8,990 9,644

4 9168 9,792

5 8,858 9,119

6 8,864 5,161

7 8,779 9,212

8 8,766 9,173

9 9,254 9,694

10 9,222 9,477

11 9,170 3,660

12 9,248 9,882

13 8,593 8,815

14 3,436 9,551

15 9,366 9,559

16 8,601 8,933

17 3,557 3,174

18 8,737 4,429

19 3,893 3,917

20 8,384 9,332

21 3,758 9,621

22 9,054 9,621

23 9,142 9,122

24 8,674 8,922

25 8,487 8,775

26 689 508

27 0 0

28 701 611

29 929 798

30 1,033 870

31 1,006 809

average 6,823 7005



Day


January


1 1,527 1,292

2 1,843 1,530

3 286 247

4 3,871 4,120

5 6,223 6,149

6 8,658 9,056

7 1,347 996

8 8,019 8,315

9 1,328 1,004

10 0 0

11 2,645 2,187

12 6,519 6,215

13 9,373 10,028

14 9,145 9,887

15 3,581 3,371

16 2,570 2,214

17 2,485 2,148

18 9,687 10,575

19 9,404 10,027

20 9,624 10,357

21 9,599 10,505

22 9,538 10,434

23 9,430 9,877

24 9,204 9,814

25 9,556 10,339

26 4,258 4,342

27 9,263 10,381

28 9,304 10,232

29 9,464 10,467

30 8,943 9,670

31 8,799 8,986

average 6,056 6,343



Day


February


1 9,321 10,074

2 2,556 2,005

3 9,464 10,377

4 9,224 10,147

5 9,280 10,223

6 9,265 9,968

7 9,136 9,743

8 9,149 9,795

9 9,004 9,726

10 8,594 9,592

11 8,966 9,881

12 8,123 9,052

13 7,934 8,804

14 8,182 9,227

15 8,585 9,582

16 8,645 9,849

17 8,079 9,260

18 4,540 4,778

19 0 0

20 8,289 9,242

21 8,322 9,176

22 8,359 9,460

23 7,182 8,083

24 8,368 9,403

25 8,349 9,493

26 5,989 6,909

27 221 48

28 220 48

29 0 0

average 7,012 7,722



3. Verification report

This verification report covers the period December 2015 – January 2016 – February2016. The MOCAGE skill scores are successively presented for four pollutants: ozone,NO2, PM10 and PM2.5. 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. The forecasts and the analyses cover a largeEuropean domain (25°W-45°E, 30°N-70°N). The statistical scores that are reported arethe root-mean-square error, the modified mean bias and the correlation. The surfaceobservations that are acquired by METEO-FRANCE and used for verification are

described in D50.1.2-2016Q1, that will be delivered shortly.

Verification of NRT forecasts

The following figures present, for each pollutant (ozone, NO2, PM10, PM2.5):

- in the upper-left panel, the root-mean square error of daily maximum (for ozone andNO2) or of daily mean (PM10) for the first-day forecasts with regards to surface

observations, for every quarter since DJF2014/2015, a target reference value isindicated as an orange line,

- in the upper-right panel, the root-mean square error of pollutant concentrationforecasts with regards to surface observations as a function of forecast term,

- in the lower-left panel, the modified mean bias of pollutant concentration forecastswith regards to surface observations as a function of forecast term,

- in the lower-right panel, the correlation of pollutant concentration forecasts withregards to surface observations as a function of forecast term.

The graphics show the performance of MOCAGE (black curves) and of the ENSEMBLE(blue curves).



MOCAGE forecasts: ozone skill scores against data from representative sites,period December 2015 - January 2016 - February 2016

The MOCAGE ozone forecasts showed a very high RMSE (around 30 µg.m-3) and a veryhigh positive bias during this winter. Complementary diagnoses showed that the biashas been increasing steadily since summer 2015, and that it is due to a bug with the

implementation of gas deposition with the new surface IFS fields after the IFS changeof cycle in April 2015. This bug does not affect significantly correlation, which remainssimilar as during winter 2014. Correlation decreases with forecast time.



MOCAGE forecasts: NO2 skill scores against data from representative sites,period December 2015 - January 2016 - February 2016

The MOCAGE NO2 forecast bias oscillates around 0, being slightly positive during night-time and slightly negative during daytime. This is different from the ENSEMBLE bias,which remains negative at every hours of the day. The MOCAGE RMSE oscillates twice

daily and follows rather well the ENSEMBLE, except in late night, when the MOCAGERMSE is much higher than the ENSEMBLE. The correlation of MOCAGE is about 0.1point below the ENSEMBLE and both scores decrease with forecast time. The RMSE ofthe NO2 daily maximum forecasts has improved significantly for MOCAGE since winter

2014 and is now very close to the ENSEMBLE performance. On other scores, theperformance of MOCAGE for NO2 forecasts remains similar compared to winter 2014.



MOCAGE forecasts: PM10 skill scores against data from representative sites,period December 2015 - January 2016 - February 2016

The MOCAGE PM10 forecast bias is strongly negative. Values are between -0.7 and-0.85, which is similar as during past winter. MOCAGE NMMB is far from the ENSEMBLENMMB (~ -0.1). MOCAGE RMSE oscillates around-17 µg.m-3, which is slightly lower

than for past winter. There is no significant tendency to increase with time. MOCAGERMSE is higher than the ENSEMBLE. MOCAGE correlation for PM10 is still poor (~0.23)and significantly lower than the ENSEMBLE. The absence of secondary aerosolformation process in MOCAGE is the main reason that explains the poor performance of

PM10 MOCAGE forecasts.



MOCAGE forecasts: PM2.5 skill scores against data from representative sites,period December 2015 - January 2016 - February 2016

The scores for PM2.5 MOCAGE forecasts are consistent with the PM10 forecasts: strongnegative bias, high RMSE compared to the ensemble and poor correlation. The absenceof secondary aerosol formation process in MOCAGE is the main reason that explains

the poor performance of PM10 MOCAGE forecasts.



Verification of NRT analyses

The following figures present, for each pollutant (ozone, NO2, PM10):

- in the upper-left panel, the root-mean square error of daily maximum (for ozone andNO2) or of daily mean (PM10) for the analyses (solid line) and for the first-day forecasts(dashed line) with regards to surface observations, for every quarter since

DJF2014/2015, a target reference value is indicated as an orange line,

- in the upper-right panel, the root-mean square error of pollutant concentration of theanalyses (solid line) and of the first-day forecasts (dashed line), with regards tosurface observations as a function of forecast term,

- in the lower-left panel, the modified mean bias of pollutant concentration forecasts ofthe analyses (solid line) and of the first-day forecasts (dashed line), with regards tosurface observations as a function of forecast term,

- in the lower-right panel, the correlation of pollutant concentration of the analyses

(solid line) and of the first-day forecasts (dashed line), with regards to surface observations as a function of forecast term.

The graphics show the performances of MOCAGE (black curves) and of the ENSEMBLE(blue curves). The superposition of the analysis scores (solid lines) and of the forecast

scores (dashed lines) computed over the same observation dataset is helpful to assessthe added value of data assimilation.



MOCAGE analyses: ozone skill scores against data from representative sites,period December 2015 - January 2016 - February 2016

The MOCAGE ozone analyses scores show a positive bias and a RMSE similar to theENSEMBLE forecasts. The correlation of MOCAGE ozone analyses are lower than theENSEMBLE. The assimilation of surface observations improves very significantly the

performance compared to the forecasts, on all scores. The bug impacting gasdeposition that was reported for MOCAGE ozone forecasts may also explain therelatively degraded scores of MOCAGE ozone analyses, in terms of bias and RMSE. Still,the assimilation of surface observations seems to limit the impact of this bug.



MOCAGE analyses: NO2 skill scores against data from representative sites,period December 2015 - January 2016 - February 2016

The performance of MOCAGE NO2 analyses daily maximum analyses is very close to theENSEMBLE and is significantly better than the MOCAGE forecasts. The daily variation ofMOCAGE RMSE is close to the ENSEMBLE, except during the late night-time. The

MOCAGE correlation is improved after assimilation of surface observations, but itremains about 0.06 below the ENSEMBLE correlation.



MOCAGE analyses: PM10 skill scores against data from representative sites,period December 2015 - January 2016 - February 2016

Neither MOCAGE nor ENSEMBLE production of PM10 analyses have been implementedyet.



Analysis of MOCAGE performances for the quarter

The scores of MOCAGE ozone forecasts have shown a strong positive bias and high

RMSE this quarter, due to a bug that in gas deposition. The ozone correlation remains

similar as during past winter, but overall MOCAGE ozone forecasts were degraded. This

bug is related to the implementation of surface deposition after a IFS change in April

2015 and it will be corrected soon. The performance of MOCAGE NO2 daily maximum is

better than past winter and very close to the ENSEMBLE performance. On other scores,

the MOCAGE NO2 forecast performance is similar as past winter. The implementation of

the TNO/MACC-III anthropogenic emission inventory will help to further improve the

MOCAGE NO2 scores.

The scores of MOCAGE PM (10 and 2.5) forecasts show, as for past quarters, a strong

negative bias and poor correlation, with regards to the ENSEMBLE performance. This

poor performance of MOCAGE for PM is due to the lack of representation of secondary

aerosols, which is expected to change very soon with the introduction of secondary

inorganic aerosols into MOCAGE.

The MOCAGE analyses shows overall a clear improvement for ozone and NO2 with

regards to the MOCAGE forecasts. MOCAGE assimilates ozone and NO2 surface

observations. The assimilation of ozone surface observations limits the impact of the

bug that was detected on ozone forecasts. The differences of scores between the

MOCAGE and ENSEMBLE analyses is lower than the differences between the MOCAGE

and ENSEMBLE forecasts. The assimilation of surface observations by MOCAGE leads to

a moderate but constant improvements of the NO2 scores during the day.


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