REPORT ABOUT ENVISAT SCIAMACHY NRT OZONE PRODUCT (SCI RV 2P) FOR JULY August 11, 2005

Similar documents
Total ozone (Dobson units) Total ozone (Dobson units) 500

Evaluation of calibration and potential for assimilation of SEVIRI radiance data from Meteosat-8

ACCOUNTING FOR THE SITUATION-DEPENDENCE OF THE AMV OBSERVATION ERROR IN THE ECMWF SYSTEM

Validation report of the MACC 43- year multi- sensor reanalysis of ozone columns, version 2 Period

OBSERVING SYSTEM EXPERIMENTS ON ATOVS ORBIT CONSTELLATIONS

Assimilation of MIPAS limb radiances at ECMWF using 1d and 2d radiative transfer models

Satellite Observations of Greenhouse Gases

Extending the use of surface-sensitive microwave channels in the ECMWF system

N. Bormann,* S. B. Healy and M. Hamrud European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire, RG2 9AX, UK

Towards a better use of AMSU over land at ECMWF

AMVs in the ECMWF system:

Representation of the stratosphere in ECMWF operations and ERA-40

WHAT CAN WE LEARN FROM THE NWP SAF ATMOSPHERIC MOTION VECTOR MONITORING?

Forecasts and assimilation experiments of the Antarctic Ozone Hole 2008

Application of PCA to IASI: An NWP Perspective. Andrew Collard, ECMWF. Acknowledgements to Tony McNally, Jean-Noel Thépaut

Table of Contents DBCP Buoy Monitoring Statistics... 1

Figure 2. Time series of the standard deviation of spatial high pass filtered QuikSCAT wind stress (solid) and AMSR SST (dashed) for (top to bottom)

Long-term time-series of height-resolved ozone for nadir-uv spectrometers: CCI and beyond

OSSE to infer the impact of Arctic AMVs extracted from highly elliptical orbit imagery

ASSIMILATION EXPERIMENTS WITH DATA FROM THREE CONICALLY SCANNING MICROWAVE INSTRUMENTS (SSMIS, AMSR-E, TMI) IN THE ECMWF SYSTEM

NUMERICAL EXPERIMENTS USING CLOUD MOTION WINDS AT ECMWF GRAEME KELLY. ECMWF, Shinfield Park, Reading ABSTRACT

ERA-40 Project Report Series

Assimilation of MIPAS limb radiances in the ECMWF system. I: Experiments with a 1-dimensional observation operator

Data Comparison Techniques

Assimilation of MIPAS limb. Part I: Experiments with a 1-dimensional observation operator. Niels Bormann and Jean-Noël Thépaut. Research Department

Assimilating only surface pressure observations in 3D and 4DVAR

Prospects for radar and lidar cloud assimilation

Parametrization of convective gusts

Using visible spectra to improve sensitivity to near-surface ozone of UV-retrieved profiles from MetOp GOME-2

Status of the ERA5 reanalysis production

Multi sensor reanalysis of total ozone

GLOBAL ATMOSPHERIC MOTION VECTOR INTER-COMPARISON STUDY

Reanalysis applications of GPS radio occultation measurements

Monitoring and Assimilation of IASI Radiances at ECMWF

D. Fonteyn BelgischInstituut voor Ruimte-Aëronomie (BIRA-IASB)

Estimates of observation errors and their correlations in clear and cloudy regions for microwave imager radiances from NWP

Use of reprocessed AMVs in the ECMWF Interim Re-analysis

THE ASSIMILATION OF SURFACE-SENSITIVE MICROWAVE SOUNDER RADIANCES AT ECMWF

On the importance of land surface emissivity to assimilate low level humidity and temperature observations over land

Application of the sub-optimal Kalman filter to ozone assimilation. Henk Eskes, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands

Use of ATOVS raw radiances in the operational assimilation system at Météo-France

Monitoring of observation errors in the assimilation of satellite ozone data

Accounting for non-gaussian observation error

Use of satellite winds at Deutscher Wetterdienst (DWD)

THE USE OF SATELLITE OBSERVATIONS IN NWP

Diagnostics of the prediction and maintenance of Euro-Atlantic blocking

Improving the use of satellite winds at the Deutscher Wetterdienst (DWD)

Experiences from implementing GPS Radio Occultations in Data Assimilation for ICON

Stability in SeaWinds Quality Control

Regional Production, Quarterly report on the daily analyses and forecasts activities, and verification of the EURAD-IM performances

THE USE OF IASI AND GOME-2 ATMOSPHERIC COMPOSITION DATA IN THE MACC-II DATA ASSIMILATION SYSTEM

Masahiro Kazumori, Takashi Kadowaki Numerical Prediction Division Japan Meteorological Agency

The ECMWF Diagnostics Explorer : A web tool to aid forecast system assessment and development

Assimilation Experiments of One-dimensional Variational Analyses with GPS/MET Refractivity

An evaluation of radiative transfer modelling error in AMSU-A data

Assimilation of precipitation-related observations into global NWP models

S3-A Winds & Waves Cyclic Performance Report. Cycle No Start date: 21/08/2016. End date: 17/09/2016

Direct assimilation of all-sky microwave radiances at ECMWF

Weak Constraints 4D-Var

CURRENT STATUS OF SCIAMACHY POLARISATION MEASUREMENTS. J.M. Krijger 1 and L.G. Tilstra 2

Niels Bormann, Graeme Kelly, and Jean-Noël Thépaut

Initial results from using ATMS and CrIS data at ECMWF

Comparison of Long-term Downward Radiation Observations at Tateno with JRA-25 and ERA-40 Data

BSC Data Assimilation Updates

CURRENT RETRIEVAL AND INTER-COMPARISONS RESULTS OF SCIAMACHY NIGHTTIME NO X

Regional Production, Quarterly report on the daily analyses and forecasts activities, and verification of the CHIMERE performances

Development of 3D Variational Assimilation System for ATOVS Data in China

Wind tracing from SEVIRI clear and overcast radiance assimilation

A study on the spread/error relationship of the COSMO-LEPS ensemble

GEMS. Nimbus 4, Nimbus7, NOAA-9, NOAA11, NOAA16, NOAA17

ESA CONTRACT REPORT. SMOS Continuous Monitoring Report - Part 1

The potential impact of ozone sensitive data from MTG-IRS

Upgrade of JMA s Typhoon Ensemble Prediction System

Revision of the ECMWF humidity analysis: Construction of a gaussian control variable

Assimilation of water vapour radiances from geostationary imagers and HIRS at ECMWF

onboard of Metop-A COSMIC Workshop 2009 Boulder, USA

Assimilation of IASI reconstructed radiances from Principal Components in AROME model

Rosemary Munro*, Graeme Kelly, Michael Rohn* and Roger Saunders

Chemistry data assimilation validation with respect to independent data

Fernando Prates. Evaluation Section. Slide 1

The role of data assimilation in atmospheric composition monitoring and forecasting

The Contribution of Locally Generated MTSat-1R Atmospheric Motion Vectors to Operational Meteorology in the Australian Region

Verification of Sciamachy s Reflectance over the Sahara J.R. Acarreta and P. Stammes

SATELLITE DATA IMPACT STUDIES AT ECMWF

On the prognostic treatment of stratospheric ozone in the Environment Canada global NWP system

Active Sensor Data Assimilation using GSI

ECMWF reanalyses: Diagnosis and application

On the impact of the assimilation of ASAR wave spectra in the wave model MFWAM

Evaluation and comparisons of FASTEM versions 2 to 5

STATUS AND DEVELOPMENT OF SATELLITE WIND MONITORING BY THE NWP SAF

Antarctic Ozone Bulletin

S3-A Wind & Wave Cyclic Performance Report. Cycle No Start date: 07/08/2017. End date: 03/09/2017

High resolution regional reanalysis over Ireland using the HARMONIE NWP model

GEMS/MACC Assimilation of IASI

Operational Rain Assimilation at ECMWF

Can the assimilation of atmospheric constituents improve the weather forecast?

S3-A Wind & Wave Cyclic Performance Report. Cycle No Start date: 13/02/2018. End date: 11/03/2018

Have a better understanding of the Tropical Cyclone Products generated at ECMWF

S3-A Wind & Wave Cyclic Performance Report. Cycle No Start date: 11/11/2016. End date: 07/12/2016

GNSS radio occultation measurements: Current status and future perspectives

Transcription:

REPORT ABOUT ENVISAT SCIAMACHY NRT OZONE PRODUCT (SCI RV 2P) Vanda da Costa Bechtold ECMWF, Shinfield Park, Reading, RG2 9AX, United Kingdom, Email: Vanda.Bechtold@ecmwf.int, Tel: 44 8 9499369 August, 5. Key points for July 5 SCIAMACHY data quality stable. SCIAMACHY data about 5 DU lower in the global mean than ECMWF ozone values. Slight increase of the (negative) global mean departures (SCIAMACHY-ECMWF) from 28 July onwards. No data on 4 July ( UTC) and July (6, 2, 8 UTC). 2. Quality and amount of received data This report covers SCIAMACHY NRT total column ozone data for July 5. Amount of received data and their quality are shown in Figures -6 for various latitude bands. Geographical distributions of mean number of data, mean observation values and mean first-guess departures are shown in Figures 7-9. Timeseries of zonal mean number of data, zonal mean observation values and zonal mean first-guess departures are shown in Figures -2. Figures 3-5 present the scatter plots of SCIAMACHY ozone values against first-guess and latitude values, as well as the scatter plot of first-guess departures of SCIAMACHY ozone values against latitude. The timeseries plots (Figures -6) show that SCIAMACHY data quality is stable in July. The global mean departures (SCIAMACHY-ECMWF) are around -5 DU, however slightly larger global mean negative biases can be seen after 28 July until the end of the month. The standard deviations of the model departures have also been stable in July. Regarding the SCIA- MACHY data standard deviations, the decrease observed from April to the end of June is not noticeable anymore. In the global mean the standard deviations of the model departures are around 5 DU, whereas the standard deviations of the SCIAMACHY data are roughly around 3 DU. There are no data on 4 July ( UTC) and July (6, 2 and 8 UTC). The geo plots, the hovmoeller plots and the scatter plots (Figures 7-5) show that the largest biases are observed at the southern high latitudes. These large mean departures are likely to occur at high solar zenith angles.

3. Remarks This monitoring report was produced with the operational ECMWF model (CY29R2). In cycle CY29R2 ozone layers from SBUV/2 on NOAA-6 and SCIAMACHY total column ozone data produced by KNMI are actively assimilated. The comparison of SCI RV 2P data against the ECMWF ozone field does not give an independent validation. All ozone values are in Dobson Units (DU). 2

Number Statistics for Ozone from ENVISAT / SCIAMACHY Layer =,. - 3.25 hpa, All Data Area: lon_w=., lon_e= 3., lat_n= 9., lat_s= -9. (all surface types) EXP =, Data Period = 5638-5738 OBS FG ANA - - - 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 OBS-FG OBS-AN 2 5 6 7 8 9 3 4 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 stdv(obs-fg) stdv(obs-an) stdv(obs) 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 n_displayed n_all n_active n_used n_not_active 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 Fig.. Time series of mean observations, first guess and analysis values (top panel), first-guess and analysis departures (second panel), standard deviations (third panel) and number of data (bottom panel) per 6-hour cycle for ENVISAT SCIAMACHY NRT ozone data for July 5 (Global means). 3

Number Statistics for Ozone from ENVISAT / SCIAMACHY Layer =,. - 3.25 hpa, All Data Area: lon_w=., lon_e= 3., lat_n= 9., lat_s=. (all surface types) EXP =, Data Period = 5638-5738 OBS FG ANA - - - 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 OBS-FG OBS-AN 2 5 6 7 8 9 3 4 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 stdv(obs-fg) stdv(obs-an) stdv(obs) 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 n_displayed n_all n_active n_used n_not_active 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 Fig. 2. As Fig. but for 9-N. 4

Number Statistics for Ozone from ENVISAT / SCIAMACHY Layer =,. - 3.25 hpa, All Data Area: lon_w=., lon_e= 3., lat_n=., lat_s= 3. (all surface types) EXP =, Data Period = 5638-5738 OBS FG ANA - - - 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 OBS-FG OBS-AN 2 5 6 7 8 9 3 4 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 stdv(obs-fg) stdv(obs-an) stdv(obs) 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 n_displayed n_all n_active n_used n_not_active 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 Fig. 3. As Fig. but for -3N. 5

Number Statistics for Ozone from ENVISAT / SCIAMACHY Layer =,. - 3.25 hpa, All Data Area: lon_w=., lon_e= 3., lat_n= 3., lat_s= -3. (all surface types) EXP =, Data Period = 5638-5738 OBS FG ANA - - - 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 OBS-FG OBS-AN 2 5 6 7 8 9 3 4 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 stdv(obs-fg) stdv(obs-an) stdv(obs) 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 n_displayed n_all n_active n_used n_not_active 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 Fig. 4. As Fig. but for 3N-3S. 6

Number Statistics for Ozone from ENVISAT / SCIAMACHY Layer =,. - 3.25 hpa, All Data Area: lon_w=., lon_e= 3., lat_n= -3., lat_s= -. (all surface types) EXP =, Data Period = 5638-5738 OBS FG ANA - - - 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 OBS-FG OBS-AN 2 5 6 7 8 9 3 4 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 stdv(obs-fg) stdv(obs-an) stdv(obs) 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 n_displayed n_all n_active n_used n_not_active 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 Fig. 5. As Fig. but for 3-S. 7

Number Statistics for Ozone from ENVISAT / SCIAMACHY Layer =,. - 3.25 hpa, All Data Area: lon_w=., lon_e= 3., lat_n= -., lat_s= -9. (all surface types) EXP =, Data Period = 5638-5738 OBS FG ANA - - - 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 OBS-FG OBS-AN 2 5 6 7 8 9 3 4 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 stdv(obs-fg) stdv(obs-an) stdv(obs) 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 n_displayed n_all n_active n_used n_not_active 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3 Fig. 6. As Fig. but for -9S. 8

STATISTICS FOR OZONE FROM ENVISAT / SCIAMACHY NUMBER OF OBSERVATIONS PER GRID SQUARE (ALL) DATA PERIOD = 57-5738 EXP =, LAYER =,. - 3.25 HPA Min: Max: 52 Mean: 7.2242 N 3 N 3 N 3 S 3 S S 5 W 5 W W W 9 W 9 W W W 3 W 3 W 3 E 3 E E E 9 E 9 E E E 5 E 5 E N S 46 43 37 34 3 28 25 22 9 6 3 7 4 Fig. 7. Geographical distribution of mean number of data for ENVISAT SCIAMACHY NRT ozone data for July 5. STATISTICS FOR OZONE FROM ENVISAT / SCIAMACHY MEAN OBSERVATION [DU] (ALL) DATA PERIOD = 57-5738 EXP =, LAYER =,. - 3.25 HPA Min: 224.65 Max: 4.46 Mean: 292.2 N 3 N 3 N 3 S 3 S S 5 W 5 W W W 9 W 9 W W W 3 W 3 W 3 E 3 E E E 9 E 9 E E E 5 E 5 E N S 525 475 45 425 375 35 325 275 25 225 75 5 Fig. 8. Geographical distribution of mean observation values for ENVISAT SCIAMACHY NRT ozone data for July 5. 9

Latitude, STATISTICS FOR OZONE FROM ENVISAT / SCIAMACHY MEAN FIRST GUESS DEPARTURE (OBS-FG) [DU] (ALL) DATA PERIOD = 57-5738 EXP =, LAYER =,. - 3.25 HPA Min: -53.2 Max: 49.57 Mean: -4.932 N 3 N 3 N 3 S 3 S S 5 W 5 W W W 9 W 9 W W W 3 W 3 W 3 E 3 E E E 9 E 9 E E E 5 E 5 E N S 9 7 5 3 - - -3 - -5 - -7 - -9 - Fig. 9. Geographical distribution of mean first-guess departures for ENVISAT SCIAMACHY NRT ozone data for July 5. STATISTICS FOR OZONE FROM ENVISAT / SCIAMACHY LAYER =,. - 3.25 HPA NUMBER OF OBSERVATIONS IN AVERAGE (ALL) EXP =, DATA PERIOD = 5638-5738 Min: Max: 9 Mean: 43.866 9 7 5 3 - - -3 - -5 - -7 - -9! " 2 # 3 $ 4 % 5 & 6 ' 7 ( 8 ) 9 * 2 " 3 # 4 $ 5 % 6 & 7 ' 8 ( 9 ) * 2 22 " 23 # 24 $ 25 % 26 & 27 ' 28 ( 29 ) 3 * 3 + ) 9 ( ' 7 & % 5 $ # 3 " * - - -3 - -5 - -7 - -9 2 97 83 69 55 4 27 3 99 85 7 57 43 29 5 Fig.. Hovmoeller diagram of zonal mean number of data for ENVISAT SCIAMACHY NRT ozone data per 6-hour cycle for July 5.

Latitude Latitude 8 8 7 7 < A @? > < ; : 9 STATISTICS FOR OZONE FROM ENVISAT / SCIAMACHY LAYER =,. - 3.25 HPA ZONAL MEAN OBSERVATION (ALL) EXP =, DATA PERIOD = 5638-5738 Min: 23.7 Max:.6 Mean: 289.2 9 7 5 3 - - -3 - -5 - -7. 6 6 6 - - -9-2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3-9 5 9 4 3 7 2 5 / 3. - - -3 - -5 - -7 < 525 < ; 475 ; 45 ; 425 ; : 375 : 35 : 325 : 9 275 9 25 9 225 9 75 5 Fig.. Hovmoeller diagram of zonal mean observation values for ENVISAT SCIAMACHY NRT ozone data per 6-hour cycle for July 5. STATISTICS FOR OZONE FROM ENVISAT / SCIAMACHY LAYER =,. - 3.25 HPA ZONAL MEAN FIRST GUESS DEPARTURE (OBS-FG) (ALL) EXP =, DATA PERIOD = 5638-5738 Min: -55.222 Max: 94.928 Mean: -4.4897 9 7 5 3 - - -3 - -5 - -7. 6 6 6 - - -9-2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 22 23 24 25 26 27 28 29 3 3-9 5 9 4 3 7 2 5 / 3. - - -3 - -5 - -7 9 7 5 3 = - - -3 - -5 - -7 - -9 - Fig. 2. Hovmoeller diagram of zonal mean first-guess departures for ENVISAT SCIAMACHY NRT ozone data per 6-hour cycle for July 5.

OBS OBS OBS-FG L K K Scatterplot of OBS versus FG SCIAMACHY on ENVISAT, total column EXP = ; Period = 57 to 5738 All Data 95 9 85 75 7 65 55 45 35 25 5 5 B B B B B B B 7 B B 9 B FG 7 75 75 5 5 2 Total number = 39825 Maximum number per bin = 8862 OBS min = 43.8 max = 445.5 FG min = 24.8 max = 452. y mean = 292. y stdev = 3.3 x mean = 296.9 x stdev = 3. BIAS (y-x) = -4.9 RMS = 6.2 corr. coef. =.872 Fig. 3. Scatter plot of ENVISAT SCIAMACHY ozone values against latitude for July 5. The colours show the number per bin, the black dots the mean values per bin. Scatterplot of Observations versus Latitude SCIAMACHY on ENVISAT, total column EXP = ; Period = 57 to 5738 All Data 95 9 85 75 7 65 55 45 35 25 5 5-9 C - D -7 E - F -5 G - H -3 I - J - B 3 5 7 9 Latitude [ deg ] 7 75 75 5 5 2 Total number = 39825 Maximum number per bin = 98 OBS min = 43.8 max = 445.5 Latitude min = -67.8 max = 85. y mean = 292. y stdev = 3.3 Fig. 4. Scatter plot of ENVISAT SCIAMACHY ozone values against latitude for July 5. The colours show the number per bin, the black dots the mean values per bin. Scatterplot of FG Departures versus Latitude SCIAMACHY on ENVISAT, total column EXP = ; Period = 57 to 5738 All Data - - - - - - - -9 C - D -7 E - F -5 G - H -3 I - J - B 3 5 7 9 Latitude [ deg ] 7 75 75 5 5 2 Total number = 39825 Maximum number per bin = 2 OBS min = 43.8 max = 445.5 FG min = 24.8 max = 452. Latitude min = -67.8 max = 85. y mean = -4.9 y stdev = 5.4 Fig. 5. Scatter plot of first-guess departures of ENVISAT SCIAMACHY ozone against latitude for July 5. The colours show the number per bin, the black dots the mean values per bin. 2