Data assimilation for local rainfall near Tokyo on 18 July 2013 using EnVAR with observation space localization
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1 Daa assimilaion for local rainfall near Tokyo on 18 July 2013 using EnVAR wih observaion space localizaion *1 Sho Yokoa, 1 Masaru Kunii, 1 Kazumasa Aonashi, 1 Seiji Origuchi, 2,1 Le Duc, 1 Takuya Kawabaa, 3,1 Tadashi Tsuyuki 1 Meeorological Research Insiue, 2 JAMSTEC, 3 Meeorological College 6 h Research Meeing of Ulrahigh Precision Mesoscale Weaher Predicion, 7 March 2016
2 Inroducion Wha is EnVAR? Daa Assimilaion Analysis x 0 is provided from Firs guess x 0 f and Observaion y. x 0 akes a maximum likelihood value when cos funcion J is minimum ( J=0). 2/14 Cos Funcion Gradien J = J 1 2 f T 1 f 1 ( x ) B ( x x ) + H M ( x ) J x Background erm T 1 [ ( ) y ] R [ H ( M ( x )) y ] Mehod Background covariance How o solve x 0 How o calculae 3DVAR,4DVAR Saisic Implicily Wih adjoin of M and H Hybrid-4DVAR Ensemble-based Implicily Wih adjoin of M and H EnKF (LETKF) Ensemble-based Explicily Ensemble approximaion EnVAR Ensemble-based Implicily Ensemble approximaion x T 0 = B 1 ( ) f H M ( ) ( x0 ) x x x 0 Observaion erm R 1 [ H ( M ( x )) y ] Several mehods are classified using how o solve J=0. EnVAR provides analysis implicily wihou adjoin models 0
3 Inroducion EnVAR wih observaion space localizaion 3/14 x a = x Analysis f + j δx f, j w j B Background error covariance 1 f f T = L δx0, jδx0, j M 1 j i: analysis poins k: observaion poins : ime slos j: ensemble members Analysis valuable is ransformed from x o w J w Gradien of J ij Then, Localizaion facor - Localizaion facor in observaion erm - Equivalen o B-localizaion - Globally defined J Observaion error variance [ ( ) ] a H y 1 + L δh R = ( M 1) w x ij k, ik Perurbaion of H kj, k, k k, Observaion operaor (non-linear) Similar o LETKF Differen from LETKF
4 Inroducion Observaion sysem simulaion experimens wih SPEEDY model Number of members: 20 Assimilaion window: 6 hours Localizaion radius: σ H =1000(km), σ V =0.1(sigma) Observaions: U, V, T, RH, Ps Inflaion: Muliplicaive (=1.1) Analysis ime: Cener of he window (=3h) Observaion ime: =1h, 3h, 5h Posiions of observaions 4/14 Bias of specific humidiy 6-hour forecas (g/kg) o naure run RMSE of specific humidiy 6-hour forecas (g/kg) o naure run Number of forecas-analysis cycles (every 6 hour) Is EnVAR beer han LETKF in real obs. daa assimilaion?
5 Experimen Local Rainfall on 18 July JST 16 17JST 17 18JST 18 19JST 19 20JST 20 21JST 5/14 Analyzed precipiaion MSM (iniial: 15JST) - Two precipiaion sysems were generaed. - Accurae forecas is difficul. (even hough he iniial condiion included rainfall) MSM (iniial: 18JST) Dense observaions are expeced o improve forecass
6 Experimen Assimilaed Dense Observaions 6/14 Observaion Elemens Frequency Surface (JMA Surface observaion and AMeDAS) U, V, T every 10 minues GNSS PWV every 10 minues Radar Radial wind every 10 minues Kashiwa, Haneda, Naria Radiosonde U, V, T, RH every 3 hours Tsukuba, Urawa, Yokosuka, Ryofu Maru Seing Horizonal localizaion: 20 km Verical localizaion: 0.1 lnp (PWV is no localized verically) Muliplicaive inflaion parameer: 1.2 Observaion error: U, V: 1 m/s T: 1 K RH: 10% PWV: 5 kg/m 2 Radial wind: 3 m/s Surface wind (m/s) 7/18 18JST GNSS PWV (kg/m 2 ) 7/18 18JST :Radar :Sonde
7 Experimen Flow of Assimilaion Experimens 7/14 Boundary condiion: JMA GSM Forecas + Weekly Ensemble Perurbaion JST JST JST JST JST JST JST Ouer Grid inerval: 10 km Grid number: 361x289x50 Ensemble size: 50 Analysis window: 3 hour (Operaional Observaions used in JMA Meso-DA every 30 minues) Downscaling 50 Members Boundary Condiion every 30 minues 09JST- Inner Grid inerval: 2 km Grid number: 200x200x60 Ensemble size: 50 Analysis window: 1 hour 15JST 16JST 17JST 18JST Exended Forecas : Ensemble Forecass : Analysis (LETKF or EnVAR) Targe: Local rain near Tokyo in JST Domain
8 Resuls 8/14 EnVAR 09JST EnVAR-NPWV (w/o PWV daa) EnVAR-NSONDE (w/o Sonde daa) 18JST Comparison of 1-h Rainfall in JST 19JST LETKF NDA (w/o any daa) Analyzed precipiaion Good impacs of PWV and Sonde daa assimilaion Domain o calculae he score Targe of sensiiviy
9 Resuls 9/14 Are Fracions Skill Scores improved? 09JST- 15JST 16JST 17JST 18JST : number densiy of observed rainfall in i-h fracion : number densiy of forecas rainfall in i-h fracion All four forecass from EnVAR analyses are beer han NDA srong rain Highresoluion rain posiion
![Resuls 10/14 Impac of Dense Observaions Rainfall in 18-19 JST EnVAR EnVAR-NPWV (w/o PWV daa) EnVAR-NSONDE (w/o Sonde daa) [EnVAR] [NDA] [EnVAR] [EnVAR-NPWV] - PWV daa](/docs-images/77/74997139/images/10-2.jpg "grealy improved rainfall forecass. - Radiosonde daa also improved weak rain forecass.")
10 Resuls 10/14 Impac of Dense Observaions Rainfall in JST EnVAR EnVAR-NPWV (w/o PWV daa) EnVAR-NSONDE (w/o Sonde daa) [EnVAR] [NDA] [EnVAR] [EnVAR-NPWV] - PWV daa grealy improved rainfall forecass. - Radiosonde daa also improved weak rain forecass. [EnVAR] [EnVAR-NSONDE] Boh PWV and radiosonde daa could improve rainfall forecass
![Resuls 11/14 EnVAR v.s. LETKF Rainfall in 18-19 JST EnVAR LETKF [EnVAR]](/docs-images/77/74997139/images/11-0.jpg "[NDA] - Difference beween EnVAR and LETKF is small Time series of RMS of")
![(O A) and (O F) of PWV in he forecas-analysis cycles [EnVAR] [LETKF] In](/docs-images/77/74997139/images/11-1.jpg "EnVAR, srong rainfall (> 15 mm/hr) forecass are slighly beer han ha of")
11 Resuls 11/14 EnVAR v.s. LETKF Rainfall in JST EnVAR LETKF [EnVAR] [NDA] - Difference beween EnVAR and LETKF is small Time series of RMS of (O A) and (O F) of PWV in he forecas-analysis cycles [EnVAR] [LETKF] In EnVAR, srong rainfall (> 15 mm/hr) forecass are slighly beer han ha of LETKF
12 Discussion Correlaion beween Rainfall and Iniial Saes Correlaion beween J and x n CORR( i, j) = m ( J )( ) m m J xm( i, j) xm( i, j) 2 ( J J ) ( x ( i, j) x ( i, j) ) m m m m 2 12/ km waer vapor and winds of EnVAR analysis i, j : grid number, m: ensemble member Large gradien J m x m : 1-h rainfall (18 19JST) averaged in his area ( i, j) : variables in 0 1 km heigh a 18JST If winds poin o he direcion of vecors in his figure, rainfall becomes sronger Low-level convergence is correlaed o rainfall inensiy Convergence Posiive correlaion of waer vapor Correlaion beween rainfall and 0 1km waer vapor and winds calculaed by 51-member EnVAR
![Discussion 13/14 Difference of Low-level variables [EnVAR] [EnVAR-NSONDE]](/docs-images/77/74997139/images/13-4.jpg "[EnVAR] [EnVAR-NPWV] 0 1 km waer vapor and winds of EnVAR analysis")
![Convergence Posiive incremen of waer vapor [EnVAR] [LETKF] Local fron?](/docs-images/77/74997139/images/13-5.jpg "Difference of 0 1km waer vapor and winds Convergence Convergence Incremen of")
13 Discussion 13/14 Difference of Low-level variables [EnVAR] [EnVAR-NSONDE] [EnVAR] [EnVAR-NPWV] 0 1 km waer vapor and winds of EnVAR analysis Convergence Posiive incremen of waer vapor [EnVAR] [LETKF] Local fron? Difference of 0 1km waer vapor and winds Convergence Convergence Incremen of low-level waer vapor and convergence makes rainfall sronger Posiive correlaion of waer vapor Correlaion beween rainfall and 0 1km waer vapor and winds calculaed by 51-member EnVAR
14 Summary We assimilaed dense obs. for he local rainfall near Tokyo 14/14 Impac of dense PWV and Radiosonde obs. PWV improved rainfall forecas hrough correcing low-level waer vapor Sonde obs. improved rainfall forecas hrough correcing low-level winds Comparison beween LETKF and EnVAR EnVAR can make he analysis which is closer o obs. han LETKF. Improvemen of rainfall forecas by using EnVAR is small Correlaion o rainfall based on ensemble forecass Low-level waer vapor and convergence made local rainfall sronger Are hese impacs general? Verificaion in longer period requires. GNSS daa were provided from he 2nd Laboraory, Meeorological Saellie and Observaion Sysem Research Deparmen. Radiosonde observaions were conduced as a par of TOMACS program. The oher observaion daa were from Japan Meeorological Agency. Our research was suppored in par by Sraegic Program for Innovaive Research (SPIRE), Field 3 (proposal number: hp and hp150214) as well as TOMACS.
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