Aeolus L2A optical properties products and assimilation in air quality models

Transcription

1 Aeolus L2A optical properties products and assimilation in air quality models Thomas Flament, Angela Benedetti, P. Martinet, E. Martins, L. El Amraoui, A. Dabas, P. Flamant Toulouse, 28 March 2017

2 Aladin, high spectral resolution lidar Optical properties of clouds and aerosols as spin off product Not optimized for this use, but HSR makes it unique Mie channel Rayleigh channel Page 2 L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017

3 L2A processor, the principle S Ray = S Mie = K ray N p E o r2 K ray N p E o r 2 Use of both channels : ability to separate molecular and particulate contributions ( ( r ) Or: ) (C 1( P,T, f )β m (r)+c 2 (f )β p (r))exp 2 (αm (u)+α p (u) du) 0 r (C 4 (P, T, f )β m (r )+C3 (f )β p (r ))exp 2 (α m (u)+α p (u)du) aerosols molecules D ou Fa e l b Fiz ea rot e -P bry 0 S ray, i =K ray N p E 0 (C 1,i X i +C 2,i Y i ) S mie, i =K mie N p E 0 ( C 4, i X i +C 3, i Y i ) Pure molecular signal Ri «Crosstalk» Xi= r2 ( r ) exp 2 (α m (u)+ α p ( u)du) dr 0 R i 1 u Pure particulate signal Ri Spectrum of the received light Y i= Ri 1 Page 3 βm (r ) L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017 βp (r ) r2 ( r ) exp 2 (αm (u)+α p (u)du) dr 0

4 From signal to optical properties Pure signals : Ri Ri Xi= βm (r ) r 2 R i 1 Ri Y i= + simulated clear sky molecular signal βp (r ) r2 r ( ) X i, simu = exp 2 (α m (u)+ α p ( u)du) dr ( 0 r βm (r ) r2 ( r ) exp 2 (α m (u) du) dr 0 R i 1 ) exp 2 (αm (u)+α p (u)du) dr 0 Ri 1 Extinction : Backscatter : β p,i, meas= Yi β X i m,i, simu X i, obs 2 =T p, sat, i 1 H (2 L p,i ) X i, simu NITWT i = Or Page 4 X i, obs X 1,simu 2 =T p,1,i 1 H (2 L p,i ) X 1, obs X i, simu α p,i= 1 1 H Δ Ri T p,1, i 1 NITWT i ( L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017 )

5 L2A, optical properties product BRC-scale product, accumulation over 12 s, or ~86 km Input scene ~ 700 km Page 5 L2A product = 8 BRC L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017

6 Feature finder and higher resolution product Feature detection based on Mie channel SNR Input scene, full resolution Feature Mask Clearsky Missing aerosol ~~ 700 kmkm 700 Page 6 L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017 False alarm Good detection

7 Feature finder and higher resolution product Feature detection based on Mie channel SNR Input scene at product resolution Feature Mask Clearsky Missing aerosol ~~ 700 kmkm 700 Page 7 L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017 False alarm Good detection

8 Optical properties at group level Input scene L2A product, sr-1.m-1 ~~ 700 kmkm 700 Page 8 L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017

9 Optical properties at group level Input scene L2A product, sr-1.m-1 ~~ 700 kmkm 700 Page 9 L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017, sr-1.m-1

10 Optical properties at group level Input scene L2A product, sr-1.m-1 ~~ 700 kmkm 700 Page 10 L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017, sr-1.m-1

11 More info about the product Poster tonight Aeolus undergoing acoustic tests, Page 11 L2A products and their assimilation Aeolus CalVal Rehearsal Workshop, 28 Mar 2017

12 AEOLUS/EARTHCARE AEROSOL ASSIMILATION STUDIES (A3S) Angela Benedetti (ECMWF) Edouard Martins, Laaziz El Amraoui, Alain Dabas (MétéoFrance)

13 Scientific motivation (I) Aeolus and EarthCARE will provide near-real-time (NRT) aerosol products from the ALADIN and ATLID lidars Data will be delivered to operational centres with interest in aerosol prediction and forecasting such as ECMWF ECMWF has been working on assimilation of profiling data from the CALIOP lidar on board of CALIPSO in the context of the Monitoring Atmospheric Composition and Climate projects (now the Copernicus Atmosphere Monitoring Service). Great interest in assessing the scientific relevance of aerosol data from Aeolus (prevalently a wind mission) and EarthCARE

14 Scientific motivation (II) ECMWF/CAMS provide daily aerosol forecasts up to day 4 MODIS Aerosol Optical Depths (AODs) are assimilated routinely but perhaps reaching a saturation point No profiling observation is currently used large uncertainty on aerosol vertical structure CALIPSO Feature mask CAMS freerunning model Model forecast AOD assimilation Flemming et al 2017 Graphics by Luke Jones CAMS interim Reanalysis

15 A3S objectives 1. Assess the developments necessary to prepare the ECMWF CompositionIntegrated Forecast System (C-IFS) s 4D-Var system for assimilation of ADMAEOLUS/EarthCARE aerosol profiles 2. Generate/select suitable demonstration lidar observational datasets as a proxy for AEOLUS/EarthCARE data from a model run (MOCAGE) and CALIPSO data 3. Develop and test the aerosol assimilation scheme to prepare for assimilation of ADM-AEOLUS/EarthCARE aerosol profiles 4. Perform feasibility studies of the profile assimilation using the demonstration datasets

16 1 6 Demonstration Data Sets (I) Sample backscatter profiles Sample Aeolus orbit

17 1 7 Demonstration datasets (II) Good agreement between input and simulated particulate backscatter

18 Single observation experiment Profile chosen for the single obs experiment (polluted area of Indian sub-continent) and extracted using CODA software (credits Sander Niemeijer, S&T) AERONET AOD observations available for verification Date: Altitude (m) Aerosol backscatter profile for ~Kanpur (81.9E,26.7N) Lidar backscatter (x10^6 sr m-1)

19 Recent technical developments Creation of a branch based on the most recent model cycle which is currently the operational cycle at ECMWF (CY43R1) Revision of lidar backscatter and extinction observation operators following major code changes related to observation operator handling in the Integrated Forecast System (still debugging, with the help of Julie Letertre-Danczak) Use of updated aerosol optical properties (credits:alessio Bozzo) Implementation of cloud screening based on model temperature (ongoing)

20 Example from the assimilation of CALIOP data at ECMWF Lidar backscatter x 1e7 (sr m)-1 Lidar backscatter x 1e7 (sr m)-1 Data: all operational data plus MODIS AOD and CALIOP Level 1.5 backscatter

21 Comparison of Median Profiles using independent HSRL lidar data MODIS assimilation only HSRL MACC-III HSRL MACC-III MODIS and CALIOP assimilation HSRL MACC-III HSRL MACC-III Median profiles in good agreement with MODIS AOT assimilation Adding CALIOP: produces relatively minor effects on median profiles tends to lower the AOT with respect to runs that assimilate only MODIS AOT slightly better agreement with HSRL Credits: Sharon Burton and Rich Ferrare (NASA Langley)

22 A3S EXPECTATIONS The CALIPSO experiments have demonstrated the feasibility of assimilation of lidar data for aerosol applications Limitations of the current system can be ascribed to relative biases between different observational datasets, as well as shortcomings in the model A3S will not be able to implement radical changes to the assimilation system, for example changes towards adjusting optical properties online during the minimization. Look-up tables will still be used. A3S will provide an ideal platform to continue the aerosol lidar assimilation work with the ECMWF IFS model in preparation for future datasets Developments towards use of Aeolus aerosol data will be usable to a large extent also for EarthCARE data