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Rolf Holland
5 years ago
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1 !"#"$%&'"(")*+,"$%-&.$&#/",.#0)&'0%0& 0--.,.)0%.*$&1*2&0%,*-+/"2.#&30-"-&0$'&0"2*-*)-&.$&40+0$!"#$%&'()(*&$+",%-./0%123456$2#$17*&'(8($+%9-./%056$2#$,(*(8:;($+,-3156$%#$<7:=>&$+%9-./%056$2#$%(*&$+%9-./%056$4#$ C?*??$+2?>?*:$D#56$%#$1(E(F($+2?>?*:$D#56$2#$,(*()(E($+2?>?*:$ D#5$6$1#$0?*&$+2?>?*:$D#56$(GF$2#$-E(=(*&$+2?>?*:$D#5

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

3 !"#$%&'$()*'%*"(+,'-$. Background error statistics 4D-Var Flow-dependent 4D-EnKF Flow-dependent Program code Complicated Simple Adjoint matrix Necessary Unnecessary Observation operator Asynchronous observations Analysis error covariance Requires tangent linear & adjoint operators Handles at each observational time Not provided Requires only a forward transform operator Handles at each observational time Explicitly provided

4 YA&Q%20%*-+/"2.#&*F*$" &K5LQE&D5PM CG5-6CC5- >A&="2*-*)- &KC=LP7QDE&32*8$'BH0-"'& ).'02M 40+0$"-"& R&ST6UTBL<GIJ SA&Q8210#"&CD>&Z)8[ &K\DQ=GE&CD]G!=PLM UA&=.2&V80).%; &KD5PE&QCP=5=CWXAAAM

5 obs w Data assimilation window 4D-EnKF Cycle 1 CO 2 conc. CO 2 (F 0 ) n=1...k w a,t t 0 t n /2 t n x a,f 1 Cycle 2 CO 2 conc. t n /2 t n t n t n /2 CO 2 (F 1 ) n=1...k w a,t x a,f 2 NEGO&&N".3/%&K%20$-1*2,M&,0%2.[ [O&&CD>&#*$#"$%20%.*$ JO&&CD>&Z)8[ O&&DH-"2(0%.*$& Cycle 3 CO 2 conc. t n x a,f 3 t n t n /2 2t n CO 2 (F 2 ) n=1...k w a,t P$&0&UT&<$IJE&%/"&*H-"2(0%.*$-&.$&0&'0%0&0--.,.)0%.*$&N.$'*N&02"&-.,8)%0$"*8-);& 0--.,.)0%"'E&0$'&%/"&0--.,.)0%.*$&N.$'*N&.$#)8'"-&0$0);-.-&%.,"A

6 obs w Data assimilation window 4D-EnKF Cycle 1 CO 2 conc. CO 2 (F 0 ) n=1...k w a,t t 0 t n /2 t n x a,f 1 Cycle 2 CO 2 conc. t n /2 t n t n t n /2 CO 2 (F 1 ) n=1...k w a,t x a,f 2 NEGO&&N".3/%&K%20$-1*2,M&,0%2.[ [O&&CD>&#*$#"$%20%.*$ JO&&CD>&Z)8[ O&&DH-"2(0%.*$& Cycle 3 CO 2 conc. t n x a,f 3 t n t n /2 2t n CO 2 (F 2 ) n=1...k w a,t =&N".3/%&,0%2.[&KNE&GM&8-"'&%*&*H%0.$&%/"&)*#0)&0$0);-.-&.$#2","$%&.-&"-%.,0%"'&0%& %/"&"$'&*1&%/"&N.$'*N&8-.$3&0))&*H-"2(0%.*$-&0$'&%/"&#*22"-+*$'.$3&H0#^32*8$'& +"2%82H0%.*$-&N.%/.$&%/"&N.$'*NA&

7 obs w Data assimilation window 4D-EnKF Cycle 1 CO 2 conc. CO 2 (F 0 ) n=1...k w a,t t 0 t n /2 t n x a,f 1 Cycle 2 CO 2 conc. t n /2 t n t n t n /2 CO 2 (F 1 ) n=1...k w a,t x a,f 2 NEGO&&N".3/%&K%20$-1*2,M&,0%2.[ [O&&CD>&#*$#"$%20%.*$ JO&&CD>&Z)8[ O&&DH-"2(0%.*$& Cycle 3 CO 2 conc. t n x a,f 3 t n t n /2 2t n CO 2 (F 2 ) n=1...k w a,t G/"&N".3/%&KNM&.-&(0).'&0%&0$;&%.,"&.$&%/"&0--.,.)0%.*$&N.$'*NA&

8 Comparison with the fixed lag Kalman Smoother Fixed lag Kalman smoother (e.g., Peters et al., 2005) uses the expanded state vector and the multiple time analysis. has major advantages in analyzing long term variations of global surface fluxes at a large scale. since the approach allows the use of large constraints obtained from a long data assimilation window (possibly) w/o serious model error growth with time and sampling errors. It might be difficult to obtain meaningful constrains from remote data on surface flux variations especially at a fine scale.

9 Comparison with the fixed lag Kalman Smoother Fixed lag Kalman smoother (e.g., Peters et al., 2005) uses the expanded state vector and the multiple time analysis. has major advantages in analyzing long term variations of global surface fluxes at a large scale. since the approach allows the use of large constraints obtained from a long data assimilation window (possibly) w/o serious model error growth with time and sampling errors. It might be difficult to obtain meaningful constrains from remote data on surface flux variations especially at a fine scale. Our method, with localized analyses, has advantages in analyzing fine scale structures in surface fluxes, if provided sufficient observational information is available in a localized space. However, when the analysis increment is too localized, some areas are possibly under-constrained.

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`E&G =--.,.)0%.*$&-"%%.$3O&&ST&0$0);-.-&N.%/&aB/*82);&#;#)"

11 5"%"*2*)*3;B#/",.-%2;&#*8+).$3&'0%0&0--.,.)0%.*$ Meteorological analysis JCDAS Radiative interaction Covariance drive (chemistry and transport) O3 analysis OMI, MLS W*N&'*"-&%/"&*F*$"&'0%0&0--.,.)0%.*$&.,+2*("&%/"&,"%"*2*)*3.#0)&0$0);-.-b&

12 C/",.-%2;B#).,0%"&,*'")-O DF*$"&1*2"#0-%&"22*2&c& &&&&&&&&&G",+"20%82"&0$0);-.-&"22*2! $:.%/*8%$?)?G7$F(B($(==&8&I(B&?G$+?GI'$ 87B#$F(B($(;7$(==&8&I(B7F5 NIES $V$N(;W7$2$7;;?;=$?X$B>7$8?F7I=$=B&II$7Y&=B=$ J.3O&G",+"20%82"&"22*2&KH.0-O-/0'"'& 0$'&!5Q<O#*$%*82M&#*,+02"'&%*& K0--.,.)0%"'M&4CT=QA& MRI

13 DF*$"&'0%0&0--.,.)0%.*$ 9!"#$%&'"'()*+,-+./0+12+3**4"453)4%(O&%/2*83/*8%&%/"&-%20%*-+/"2"A 9!"#$%&'"'()*+,-+1.!6712+3**4"453)4%(O&*$);&.$&%/"&)*N"2&-%20%*-+/"2"A 9 ]*&.,+2*(","$%&.$&%/"&,"-*-+/"2"&'8"&%*&%**&-,0))&"$-",H)"&-+2"0'A NIES J.3O&DS&"22*2&KH.0-O-/0'"'&0$'&!5Q<O#*$%*82M&(-A&5LQ MRI Pressure [hpa] Pressure [hpa] Free MLS assim. OMI assim. MLS+OMI assim.

14 Pressure [hpa] Ozone data assimilation (vs. Sonde, 60S-90S) August-September, ,-./&'()*!"#$%&'()* 01$2%&'()* MRI OBS JCDAS MLS+JCDAS OMI+JCDAS OMI+MLS+JCDAS

15 Improvements of temperature and o3 biases by O3 data assimilation T Bias 9 G/"&%",+"20%82"&H.0-&*1&%/"& ]P<Q&,*'")&/0-&H""$&,*-%);&& K0H*8%&d?&eM&2",*("'&H;& *F*$"&'0%0&0--.,.)0%.*$A O3 Bias 9 G/"&.,+0#%&.-&$*%&*H(.*8-&.$& 5!P&,*'")E&-.$#"&%/"&5!P&,*'")&/0-&("2;&-,0))&H.0-&.$& %/"&1*2"#0-%"'&*F*$"&Z.")'-A

16 ]P<Q 5!P 9 ]P<Q&.$&%/"&,.'')"&-%20%*-+/"2"O&DS&'0%0&0--.,.)0%.*$&c&)"--&+*-.%.("&DS&H.0-& c&y?&e&2"'8#%.*$&.$&q:&/"0%.$3&c&u?e&2"'8#%.*$&.$&$"%&20'.0%.("&/"0%.$3& 9 5!PO&=),*-%&$*&.,+0#%&*$&%/"&/"0%&H8'3"%A&G/"&20'.0%.("&-#/","&$""'-&%*&H"&.,+2*("'&%*&2",*("&%/"&#*)'&H.0-A Q.,8)0%"'&*S J.3O&!0'.0%.("&1*2#.$3&H;&Q:&R&L: N6&DS&0--.,A SW +LW SW +LW LW SW LW SW

17 7-days Hindcast experiments using the assimilated field 9 P$.%.0).F0%.*$&N.%/&0--.,.)0%"'&Z.")'-A &&&&&&&&&&&&c&w.3/&v80).%;&1*2"#0-%&kf&se&!5q<e&f&ye&h.0-m& 7"21*2,0$#"&*1&%/"&%*%0)&*F*$"&/.$'#0-%&28$-&K&(-A&D5PE&S?]Ba?]M >??a&?gay?&h&>??a&?@a>d& )*$+&'(!"#$%&'(

18 >A&="2*-*)- J*2"#0-%&,*'")-O&5=QP]\=! =--.,.)0%"'&'0%0O&&Q0%")).%"&KC=LP7QD6C=LPD7M&0$'&32*8$'BH0-"'&).'02 C*$%2*)&(02.0H)"-O&&'8-%&K+02%.%.*$"'&.$%*&Y?B-.F"&H.$-ME&'8-%&Z)8[E&-"0B -0)%E&DCE&iCE&0$'&-8)10%"&0"2*-*)- =--.,.)0%.*$&-"%%.$3O&&UT&0$0);-.-&N.%/&UgB/&%.,"&N.$'*N

32%.>6>%>??=5=2>C3-%F30%=54031=-D% 6@/=0%34/0>C3->2%.,?

19 *+,-$./%$0&1(")&+-,1#2./% No aerosol data assimilation 9*#%H>-6?%63%,C2=I/%>/03?32%.>6>%>??=5=2>C3-%F30%=54031=-D% JKL%>-.%A2=5>6/%?=5,2>C3-?;%

20 3%45&6/-&$,-/"/0&$%$07"#" M :@/%*3./2%3F%#/03?32%$4/A=/?%=-% 6@/%N237>2%#653?4@/0/% &8!9:;<!=(%3F%*)"O9*#%?=5,2>6/?P > 1(")%&4>0CC3-/.%=-63%QRS?=I/% 7=-?(E%?/>S?>26E%TGE%!GE%>-.%"(06$),& >/03?32?

21 3%45&6/-&$,-/"/0&$%$07"#" Satellite Lidar observation (CALIPSO/CALIOP): NASA launched the polarorbit satellite in Ground-based lidar network (NIES AD-Net): NIES Japan is operating more than 20 lidar stations in East Asia.

22 3%45&6/-&$,-/"/0&$%$07"#" G/"&'0%0&0--.,.)0%.*$&2"-8)%&,0^"-&%/"&'8-%&+)8,"&,*-%);&).,.%"'&%*&%/"&02"0&*1&%/"&2"'& N"0%/"2&-%0%.*$-E&.$&#*$%20-%&%*&%/"&12""&,*'")B28$&2"-8)%&.$&N/.#/&%/"&'8-%&+)8,"&#*("2-& H*%/&2"'&0$'&H)8"&-%0%.*$-A Contours and gray shades are surface dust concentrations. (a) Free model-run result without data assimilation. (b) CALIPSO data assimilation result. Sekiyama et al., ACP (2010) Red and blue circles are weather stations. Red ones observed aeolian dust. Blue ones did not observe any dust events.

23 3%45&6/-&$,-/"/0&$%$07"#" U,?6%/5=??=3-%=-1/0?/%>->28?=?%78%+-VW Sekiyama et al., SOLA (2011) G/"&'8-%&#*$#"$%20%.*$-&.$&%/"&'*N$N.$'&2"3.*$&02"&"(.'"$%);&.,+2*("'&N/"$&%/.-& '8-%&",.--.*$&0$0);-.-&.-&.$-%0))"'&%*&%/"&,*'")&-.,8)0%.*$A

24 MAB,&3%45&$,-/"/0&$%$07","&$"&#%#.$0&2/%1#./%"& /6&$,-/"/0&+-,1#2./%C& #/03?32%-,$%$07"#"Z >1>=2>72/%F30%A2=5>6/%53./2=-D[ #/03?32%20#D$)/0/E7&&./6>=2/.(Z >1>=2>72/%F30%JKL[ "./>228E%F,$)B,-G2B,D#")-7&A3,42/.%U#P

25 SA&Q8210#"&CD>&Z)8[ 9 "-%0H).-/&0&UTB<$IJ&'0%0&0--.,.)0%.*$&-;-%",&%*&"-%.,0%"& 3)*H0)&-8210#"&CD>&Z)8["-&12*,&(02.*8-&'0%0A 9 "(0)80%"&%/"&+*%"$%.0)&.,+0#%-&*1&(02.*8-&'0%0&*H%0.$"'&12*,& %/"&-8210#"&$"%N*2^&,"0-82","$%-E&\DQ=G&,"0-82","$%-E& 0$'&CD]G!=PL&0.2#201%&,"0-82","$%-&KDQQ<-MA 9!"0)&'0%0&0--.,.)0%.*$&N.%/&0&H.0-B#*22"#%"'&\DQ=G&'0%0A J*2"#0-%&,*'")-O&54@gBCTG5E&J!C\C&=CG5 =--.,.)0%"'&'0%0O&&Q0%")).%"&K\DQ=GME&=.2#201%&KCD]G!=PLME& &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\2*8$'BH0-"'&$"%N*2^ C*$%2*)&(02.0H)"-O&&Q8210#"&CD>&Z)8[E&=%,*-+/"2.#&CD>&#*$#"$%20%.*$ =--.,.)0%.*$&-"%%.$3O&&UT&0$0);-.-&N.%/&j>B/&%.,"&N.$'*N

26 4D data assimilation system for carbon cycle Simultaneously estimate atmospheric concentrations and surface fluxes of CO 2 at high spatial (2.8 ) and temporal (1.5 days) resolution. 8%$'93*)+"%:'5*O&KYM&J!C\C&=CG5&K5.;0F0^.&"%&0)AE&>??gM &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&K>M&54@gBCTG5 ;3)3+3**4"453)4%(O&&UTB<$IJ&N.%/&)*#0).F0%.*$&KL<GIJE&W8$%&"%&0)AE& >??jm 9 Q%0%"&083,"$%0%.*$&0++2*0#/ 9 C*$'.%.*$0)&.$Z)0%.*$&1*2&-8210#"&Z)8[ 9 T0%0&0--.,.)0%.*$&N.$'*NO&SB'0; 9 Q8+"2&*H-"2(0%.*$&1*2&CD]G!=PL&'0%0 9 L*$3&("2%.#0)&)*#0).F0%.*$&)"$3%/&1*2&CD]G!=PL& 9 Ug&"$-",H)"&,",H"2-

27 Number of observations per per month (a) Surface: flask (b) Surface: continuous Observation (c) CONTRAIL: vertical profile (d) CONTRAIL: upper air system simulation experiments (OSSEs) (e) GOSAT 9'",*$-%20%"&%/"&+"21*2,0$#"&*1& %/"&T=&-#/"," 9%"))&8-&/*N&,8#/&"22*2& 2"'8#%.*$-&#0$&H"&"[+"#%"'&H;& "0#/&'0%0-"%

28 Flux error reduction rate [%]: grid-scale (a) Surface network (b) GOSAT (c) CONTRAIL (d) All

29 100 Flux error reduction rate [%]: regional-fluxes Error reduction rate [%] Boreal N.America Temperate N.Ame Tropical S.America Temperate S.Ame N.Africa S. Africa Boreal Asia Temperate Asia Tropical Asia Australia Europe Error reduction rate [%] North Pacific Tropical W. Pacific Tropical E. Pacific S. Pacific Northern Ocean N. Atlantic Tropical Atlantic S.Atlantic Southern Ocean SURFACE GOSAT CONTRAIL N. Indian ocean CD]G!=PL&'0%0O&<82*+"&0$'&=-.0A& \DQ=G&'0%0O&]*2%/&0$'&Q*8%/&=,"2.#0E&Q*8%/&=12.#0E&=-.0E&0$'&<82*+" ALL S. Indian ocean

30 100 Flux error reduction rate [%]: regional-fluxes Error reduction rate [%] Boreal N.America Temperate N.Ame Tropical S.America Temperate S.Ame N.Africa S. Africa Boreal Asia Temperate Asia Tropical Asia Australia Europe Error reduction rate [%] North Pacific Tropical W. Pacific Tropical E. Pacific S. Pacific Northern Ocean N. Atlantic Tropical Atlantic S.Atlantic Southern Ocean SURFACE GOSAT CONTRAIL =''.%.*$0)&#*$-%20.$%-&02"&2"V8.2"'&"-+"#.0));&*("2&]*2%/&=12.#0E&%2*+.#0)&Q*8%/& =,"2.#0E&-*8%/"2$&]*2%/&=,"2.#0E&0$'&%/"&*#"0$-A ALL N. Indian ocean S. Indian ocean

31 J.3O&5"0$&-"0-*$0)&#;#)"-&*1&CD > & #*$#"$%20%.*$&0$'&k YS C&0%&"0#/&)0%.%8'"&H0$'A& &=.2&-0,+).$3&*$&H*02'&#*,,"2#.0)& J82%/"2&#*$-%20.$%-&N.))& H"&0''"'AAA

32 Sensitivity to OSSE settings and model/obs errors 9 G/"&DQQ<&2"-8)%-&02"&-"$-.%.("&%*&.%-&-"%%.$3-&K"A3AE&.$.%.0)& 0$'&%28"&Z)8["-E&0$'&*H-"2(0%.*$&"22*2-MA& 9 50$;&-*82#"-&*1&"22*2&0--*#.0%"'&N.%/&%/"&*H-"2(0%.*$-& 0$'&%/"&%20$-+*2%&,*'")&N.))&-%2*$3);&'"#2"0-"&%/"& 8-"18)$"--&*1&"0#/&*H-"2(0%.*$l&%/.-&#*8)'&H"#*,"&0& ).,.%.$3&10#%*2&.$&%/"&'0%0&0--.,.)0%.*$&%/0%&.$(*)("-&2"0)& '0%0&K5.;0F0^.&"%&0)AE&>?YYMA& 9 <A3AE&2"0).-%.#&-;-%",0%.#&"22*2-&.$&%/"&\DQ=G&'0%0&#0$& 2"'8#"&%/"&8-"18)$"--&*1&%/"&*H-"2(0%.*$-&H;&0&10#%*2&*1&>A

$A bias correction scheme for GOSAT 9 5*$%/);&,"0$&H.0-&*1&\DQ=G&.-&"-%.,0%"'&H;&#*,+02.$3&\DQ=G&mCD >& KL>Q:P!M&N.$

33 A bias correction scheme for GOSAT 9 5*$%/);&,"0$&H.0-&*1&\DQ=G&.-&"-%.,0%"'&H;&#*,+02.$3&\DQ=G&mCD >& KL>Q:P!M&N.%/&45=&CD>&0$0);-.-&KH0-"'&*$&-8210#"&*H-"2(0%.*$-MA 9 W*%&-+*%&Z)8["-&02"&182%/"2&0'n8-%"'&8-.$3&-822*8$'.$3&'0%0A

$Real data assimilation 9 i.0-b#*22"#%"'&\dq=g&'0%0& +2*(.'"&0''.%.*$0)& #*22"#%.$

34 Real data assimilation 9 i.0-b#*22"#%"'&\dq=g&'0%0& +2*(.'"&0''.%.*$0)& #*22"#%.*$-&0$'&.,+2*("& %/"&032"","$%&N.%/&.$'"+"$'"$%&'0%0A

35 CO 2 Analysis Results Jul 2009 Surface CO2 concentrations RMSE=3.96ppm JMA Analysis w/o assim. GOSAT assim. Flask assim. RMSE=3.12ppm RMSE=3.83ppm

$, 2010) (2) JAMSTEC ACTM (Miyazaki et al, 2008) W=DZ%GTX%*=\=-D%)>C3%#-35>28%=-%6@/%23H/0%60343?4@/0/ '<=<>+9?%49'+%@+&'$)4935+:4@@A*4%(+*9?$

36 Multi-model comparison of surface CO 2 flux estimates Forecast models: (1) MRI CDTM (Maki et al., 2010) (2) JAMSTEC ACTM (Miyazaki et al, 2008) W=DZ%GTX%*=\=-D%)>C3%#-35>28%=-%6@/%23H/0%60343?4@/0/ '<=<>+9?%49'+%@+&'$)4935+:4@@A*4%(+*9?'"'+53$='5-+3@@'9)*+ *4"A53)4%(B3**4"453)4%(+$'*A5)*<

37 4. Air quality 8%$'93*)+"%:'5*O& $+H5$CW=Q<!$+1:F?$7B$(I#6$JKKJ5 $$$$$$$$$$$$$$$$2MJNOJ$$$$PQB?PRO$>S( $$$$$$$$$$$$$$$$$2MJNUT$$$PQB?PRK#KH$>S( 6/-&-,$%$07"#"HD/%#)/-#%E&/6&!"#$%HE0/I$0& $)D/"+B,-#2&,%J#-/%D,%) C**4"453)':+:3)3O&&D5P&]D>E&QCP=5=CWX&]D>E&G<Q&DSE&"%#AAA D%()$%5+&3$43,5'*O&&]D[&",.--.*$E&]D>E&DSE&W]DSE&"%#AAA C**4"453)4%(+*'))4(=*O&STB0$0);-.-

38 A priori: east Asia A posteriori: east Asia A priori: Europe A posteriori: Europe Surface NOx emission

39 Summary :"&/0("&'"(")*+"'&#/",.#0)&'0%0&0--.,.)0%.*$&-;-%",-&%*& 0$0);F"&=-.0$63)*H0)&0%,*-+/"2.#&"$(.2*$,"$%E&8-.$3&0&-0,"& -#/","A& 9 5"%"*2*)*3;B#/",.-%2;&#*8+).$3&'0%0&0--.,.)0%.*$ 9 1*2&H"%%"2&8$'"2-%0$'.$3&-%20%*-+/"2.#&+2*#"--"-6 -%20%*-+/"2.#&2"0$0);-.-bb 9 ="2*-*)-&0$0);-.-61*2"#0-% 9 1*2&*+"20%.*$0)&8-"&0%&45=AAAb 9 W.3/&2"-*)8%.*$&-8210#"&CD >&&Z)8[&"-%.,0%"-& 9 K%/"&^$*N)"'3"&*H%0.$"'&12*,&%/"&DQQ<-&N.))&/")+&%*&.$%"2+2"%&%/"&2"0)&'0%0&0--.,.)0%.*$&2"-8)%-M 9 0%,*-+/"2.#&#*$#"$%20%.*$-&0$'&",.--.*$-&*1&+*))8%"'&30--"- 9 1*2&,*$.%*2.$3&=-.0$63)*H0)&0%,*-+/"2.#&"$(.2*$,"$%A... more data will be included...

40 Miyazaki et al., Assessing the impact of satellite, aircraft, and surface observations on CO 2 flux estimation using an ensemble-based 4D data assimilation system, JGR, (CO 2 DA system, OSSE study) Miyazaki, Performance of a local ensemble transform Kalman filter for the analysis of atmospheric circulation and distribution of long-live tracers under idealized conditions, JGR, (CO 2 DA system, OSSE study) Miyazaki et al., Formation mechanisms of latitudinal CO 2 gradient in the upper troposphere over the subtropics and tropics, JGR., 2009 (CO 2 transport/contrail analysis) Miyazaki, et al., Global-scale transport of carbon dioxide in the troposphere, JGR, (CO 2 transport/modeling) Miyazaki et al., Global NOx emission estimates from data assimilation of OMI tropospheric NO 2 columns, to be submitted (air quality DA) Maki et al., New techniques to analyze global distributions of CO 2 concentrations and fluxes from non-processed observational data, Tellus, (QC processes) Maki et al., The Impact of Ground-Based Observations on the Inverse Technique of Aeolian Dust Aerosol, SOLA, (Dust inversion) Sekiyama et al., Data assimilation of CALIPSO aerosol observations. ACP, (Aerosols assimilation) Sekiyama et al., The Effects of Snow Cover and Soil Moisture on Asian Dust: II. Emission Estimation by Lidar Data Assimilation, SOLA, (Aerosols assimilation) Iwasaki et al., Comparisons of Brewer-Dobson Circulations diagnosed from Reanalyses, JSMJ, (BD inter-comparison among reanalyses) Presentation at IUGG: Nakamura et al., Data Assimilation session (JM02). (Strat. O 3) and more soon... (hopefully!!)

Data Assimilation of Satellite Lidar Aerosol Observations

Data Assimilation of Satellite Lidar Aerosol Observations Thomas Sekiyama Meteorological Research Institute Japan Meteorological Agency (MRI/JMA) AICS Data Assimilation Workshop, 27 February 2013, Kobe,