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1 h ô 269 up d ïè 2008 SoWMEX ó ïž Áó åž õ g x r Á æ µ Õ v Œ ½ Õ v Œ n½ î ½ Å Ê oh ½ y National Center for Atmospheric Research NCAR ³p õé ÄÅ(Variational Doppler Radar Analysis System VDRAS Š p f ò / f õá (thunderstorm) f h õäžìˆ fî ÄÎ(supercell)Ñ Æ³ hh ˆwh ³v Œ ½ o VDRAS ~ Ð ô ˆÐ ã ³ x ³äž œ³ õ Œ o VDRAS ³õ zu h³ Œ ½ æ o õ ³ ä ÄÅ~ ˆŒ ³á ö 2008 v SoWMEX (Southwest Monsoon Experiment ä o IOP8 Intensive Observation Period 8 o äžœ 6 Œ 14 ³ ü ÄÅ ½ ƒ½ ž¾ Ñ Ð Ê y³ f ìˆw ÍŠf Åé š(4dvar) h ëp Í g v S-band õ š x äžì ˆ Ú n w j x ³ f fõá f Š ò i Ü xoˆ f³x o VDRAS fñ Œ xå Î ³ WRF wõáå òå xo f ³t ä äžìˆx³ f º VDRAS Îh o ÄÅ ³ Å ³ pu f³ Ñ pîî Ðz áº VDRAS Î Œ x ~³

2 270 æ p fˆ VDRAS woˆõ ³ äæªo ¼ ˆ Œ ý³ƒƒ ùo ošä å ³ f Equitable Threshold Score (ETS)x 0.1~0.2 io VDRAS Ñ WRF Å x õá f x ò «VDRAS WRF ³ f Œx ³ Œ ½ ³ n Œ fñäž ³ e õáìˆ Ñ fš ³ Ë ýn Åé õé ÄÅ Í Ð u x o ü ä õ³ëh oˆõ ¾ ë iîœò Ã¹ Œnù f Quantitative Precipitation Forecast, QPF Ëìå oˆ ž t Œxh³ ë õ ä ž Œ Š ½ å v³ oˆ pv o pvh ÄÅ³ Œxi³ƒ ƒî Ìi Å õäžìˆ x Ñ šwv Ñˆ w f ì ˆ ³ ½ õìˆ ³ˆš ä Œ Å é Three-Dimensional Variational Method 3DVAR Åé Four-Dimensional Variational Method 4DVAR Ä š Ensemble Kalman Filter EnKF i Snyder and Zhang (2003) Dowell et al. (2004) Xiao et al. (2005, 2007) Hu et al. (2006) Sun (2005a) Kawabata et al. (2007) ³ ½Å õ ìˆ j ˆ h f w oˆnù f Œ ³x Ê oh ½ y National Center for Atmospheric Research NCAR ³ õé ÄÅ Variational Doppler Radar Analysis System; VDRAS 4DVAR ˆš h õäžìˆ Sun and Crook (2001) o ÄÅø WSR-88D õäžõá Š³ p f Crook and Sun (2002, 2004)o ~ 2000 v iõœ Šh f VDRAS Ñ Ê ˆ åhvi õ Š Ž ë Ìi ô³ ½)Warner et al. 2007* Sun and Zhang (2008) ùo International H 2 O Project (IHOP)o Œ ˆÊ ä hv ³ ÆÄ Å o f³î ½Å º VDRAS ½Ñ ~ Œ ý³á ˆæÄÅ p Œ Œå xî i o Ð x ã ³ f äž ˆ œ³ Î ÐÓv Œ ½ æ VDRAS ÄÅ Å 2008 vä o Southwest Monsoon Experiment; SoWMEX äžìˆ iƒ½ ž¾ õ ùo ü õá nù f h o VDRAS fåõ óƒv ÄÅ ùvÿ ç x Ñ NCAR ³p Weather Research and Forecasting Model (WRF Å õá f xëò ³

3 h ô 271 xå Î õ ƒ oˆnù f³ã ¹v VDRAS x~ n Œ fñäž e Ë ô d Œ ½ ³ Ðˆ 2008 SoWMEX IOP8 Intensive Observation Period 8 ³ ü IOP8 äžœ 2008 v 6 Œ UTC Ð 6 Œ UTC Ì 6 Œ UTC ³ 850 hpa ³Åäh 1 ³ g y ž³ œ ô œr Ñtgœrœ Œ ä á Œº º Œ pvo ÄÅˆ ˆ ¼ ä õ oð h³ e³ ˆ ä Ñp ùšõõ 100 Ð 150 mm p šœ Ä½ ù Ðõ 200 mm 1154 UTC 1254 UTC Ñ 1354 UTC ³g Ñ Í õ h š CV 2 ³ v A A B B C C A B C v6Œ UTC 850hPah 2 ÍÑg õ h š Š (a) 1154 UTC (b) 1254 UTC (c) 1354 UTC x š dbz º š 30 Ð 60dBZ e 10dBZ

4 272 ô Œ ³Æo ÄÅ Š Œ ½ ³ ääå o A B C Æªo ˆˆ õáåç y ³ A Æªo ˆ õäžï š Ø n ³Å oˆ xè³ f yœ n¼v³x æ i¾ ( )}f Œ ½ ³ õìˆ ÄÅ Ê NCAR ³p õé ÄÅ VDRAS oæäåæä³ç ä Sun and Crook (1997) Crook and Sun (2002) Ñ Sun (2005 eœ äî ƒ½ äž¾ ìˆx ÍŠf Åé ˆš Š ½ å v³ õäžìˆ x f Ì ÄÅ³ ä ¼ h )1) ìˆw x ƒ½ äž¾ ä õäžìˆ (2)ìˆ ÊÜ ìˆ³ ìƒà QC ƒ ÍŠf â (3) Åé ìˆ w ½ w adjoint model x½ ˆ cost function ³ o (4) Å Ñ f õá æˆ f º ˆ VDRAS ä õäž x Ñ šìˆ ˆé åà ˆ wáº = T 1 ( ) ( ) 0 b 0 b o 2 o 2 + α ( v v ) α ( q q ) J + + v r r q r r P J x x B x x æ (1) p w(1) ³ x á wª}éˆ ³ 0 á ¾ j³š b áº ÑÍ Šf ³éˆ ³ o äž³éù ³ T ³óÊ Ü B ÍŠfçt ˆ t v q áx Ñ äžçtvˆ³ ˆ ô éˆ ˆ v r o õäž x v r o w f óƒì x f óƒ wi x x y y rad rad v = u+ v r r r z zrad + ( w V ) T r (2) x y z äž Ê x rad y rad z rad õ Ê u v w w Å f V T Ä¾Öõ ms -1 q rñ q r o w Ñ õäž³ -1 g kg q r o õäž³ š( )åàx i Kessler (1969) Sun and Crook (1997 o q r 1 [( η 43.1) /17.5] = 10 (3) ρ w š ˆ dbz ½ ov kg m -3 h 2 w ³ Kq áº½ ÑŠ ³vŸ~Ê (penalty term) ä é woˆäž³ ò vÿ VDRAS ³ ä ³ ˆ õäž ìˆ ä ½ ³äžìˆ ƒ ÍŠf õ oˆ w³á Ìå õ ƒ ˆäžï ³ É oãn Äõìˆ Ø î Špäõõ iƒ ½ sounding ž¾ surface station ³ìˆ fâ Å½ ìˆ½é e Œ ½ ä Crook and Sun 2002 nä

5 h ô w Á äžñ fìˆ ³ Ê xá x v p ÒØ ƒ½¾ˆ Á h Î ï ùä õá ˆš oh ³ìˆõá ƒ x Å½ ³ÍŠf 3 Ç Œ ½ w³ Á äž õ¾ ƒ½¾ äž¾ ìˆ ³ ( )ñê i¾ w f³ÿ¹z wò ùy Ñäžùõá ò Ú çt æ f Ÿ¹ äž ù ìˆ Ÿ h ëp³ð ù¾ååìˆ ˆ Á Ð r ö ˆ Á ³Ð ù¾õá ù Åå t Ë 4 Ìxˆ Ž Barnes n ä š Barnes, 1973 o ˆ wæ f ùìˆ Ð ù¾³ Ê x x ån 10 ù 4. h ëpˆ Á Ð ù¾ ¾ Ê x x v Ð 250 Ð 3750 p 500 p Ž f x ˆ wâ ë ½ ƒ ³ x ETS

6 274 Equitable Threat Score; Schaefer 1990 õá fž ETS ƒ ª ³{š åà w ¹ ž ³Î ETS ˆ á w f Ÿ¹ nëi ETS = H R + F O H R æ (4) w³ F ž ³ ˆ O äž ³ ˆ H á fñäž³œ ³ ˆ R = FO/N ˆw ¹ª ³ ˆ N f ˆÑäž ˆ q VDRAS e¼ ( ) f î VDRAS w³ y ˆ Å v Æ v væ š x ˆ 264 y ˆ 216 ï³ 2.0 ù vá 528 ù 432 ù hð p œr tgœr ô hv œe 3 d ˆ ³ p v 0.25 ù Æ ï 0.5 ù 30 p w p v Ä ù o åå Sun and Zhang (2008)ƒ y ¼v cycling Procedure ˆ Š f Ú wˆ¼wo äžìå y õð Œ ˆƒ f Ÿ¹z Œ ½ ¾ Ç Åé n f hðy 1046UTC j n Ð 1154 UTC ŠÅŒ n x f æä ¼ Ë 5 œr r xã rs â œ Ðˆ ëäž xh³ ä ž iƒ½¾ ž¾ hhåêˆ i 3 x ƒƒ ³h ª} o ˆ VDRAS ç pvíšf fâ õä 5. VDRAS y ¼v cycling Procedure Ñ WRF wå º

h ô 275 žìˆ³½é w j f ³ùä Ÿ Œ ³ pvíšf o ˆ få oœ n³x õá zo (1) CTL o äž ìˆ ƒ½ äž¾ õánä xx ÍŠf õìˆõá Ñ f (2) NCP NCEP Global Final Analyses FNL å v 1.0 1.

125 ˆ 1200 UTC f y h d Æ i 3 Ü ƒ½ìˆ Åõnä xx ÍŠf õì ˆõá Ñ f ( )e¼» ˆ Äo ³ f Œ ³ y ŒÁ CTL o oˆ VDRAS ³ Å ˆ 1154 UTC õáåç y h š 6 Ñ Š äž³ h š 2a ò å VDRAS Îh ¹ õìå ƒƒ äæo ÄÅ³

7 h ô 275 žìˆ³½é w j f ³ùä Ÿ Œ ³ pvíšf o ˆ få oœ n³x õá zo (1) CTL o äž ìˆ ƒ½ äž¾ õánä xx ÍŠf õìˆõá Ñ f (2) NCP NCEP Global Final Analyses FNL å v ˆ 1200 UTC f y h d Æ i 3 Ü ƒ½ ìˆ Åõnä xx ÍŠf õìˆõá Ñ f (3) ECM ECMWF Atmospheric Model å v ˆ 1200 UTC f y h d Æ i 3 Ü ƒ½ìˆ Åõnä xx ÍŠf õì ˆõá Ñ f ( )e¼» ˆ Äo ³ f Œ ³ y ŒÁ CTL o oˆ VDRAS ³ Å ˆ 1154 UTC õáåç y h š 6 Ñ Š äž³ h š 2a ò å VDRAS Îh ¹ õìå ƒƒ äæo ÄÅ³ wvñ Ê i ò õìˆ x få ³é ³ Œ õìˆ ÍŠf 7a ³ v fò hhé ò f ù ò w³ ehä 10-5 s t -1 À yw o ç õìˆ x 7b v f xhopv³é w³ò fù òíšf Ä 100 º õìˆîhu wå opv f CTL o ˆ 1154UTC f h š x š dbz º 30 Ð 50dBZ 10dBZ CTL o ˆ w p 0.25 ù v f ù ò f x 10-3 s -1 (a)íšf (b) 4DVAR õìˆx³ f

8 276 ³Äyé gõ opvo ÄÅ³Î Îh Ä äo f Ñy éˆ ³ Í ö Šõõ ä ôœe³ B Æªo þn Á Å 120 Ð 121 v Æ 22 Ð 23.2 v 1154 UTC t B Æªo äx ˆ¼ i 8a À ˆ o 8a d õvf â NW-SE Æ õ o Î¼ ˆ ³ õ oæ Š o Î ¼ ˆ ³x ò ³ õ Ü Æ w š > 40 dbz xx õ i ä p Z = 0.25 ù ³ v fñò f 8b o Î ˆ ³ò õ Ñd õ x øê ³ yò f äo p ³p ò ˆ 8b x ~333 ù y ~ ùü pî³ ÊÑî õç VDRAS šœå x³î Œ ÎÚ õoˆ f³äž x xx ³å{ õ Å Ñ Sun et al. (2010)o VDRAS ~ 2008 v iõœ x f³ ³ Ð³ f ³åçh 8a NW-SE Æ ä ³d Å Å ³ VDRAS o Î¼ xˆ³ cold pool ë i 9 ˆÆ º õáº VDRAS h ÄÅ³Œ t ( )» ffˆ CTL NCP ECM Äo ³Å á ò ³t äo CTL o f 60 ÿš 10 Ñäž 2 ³ h š A Æªo Ê¼y B C Æª æ p o ³ ƒôµo Ê õ ˆ A Æªo õäžá ³ šœân ³äž Ð õìˆx šx ƒôµo³ h ÄÅÅ õìˆoˆo Ä Åäžn z o ~ f³å Ì f 2 ošx w ƒƒ B Æªo Ê žw³î C Æªo t n œ ÑäžŒòhtï ò NCP f 60 ÿ 11a woˆ B C Æo ³ Ê fá Òi Æo A ˆ õäžá šn Á t åƒôæo A Ü ³ µ o³ š wv ˆ 30 Ð 40 dbz Ì 120 ÿ³ f 11b C Æo i CTL o Œœ {x šwvgwuvõ ˆ A B Æo o iäžœ žwî õyn oˆ få Œ n³ x x ECM o Ñ o ³ ˆ ˆ w 60 ÿ ³ f 12a Œxi ³á ç Œ f³ f 120 ÿš 12b ECM o f šwvò NCP 10b ƒôäž B Æ ªo f³ Ê ÑäžÅ Ðˆ o C Æªo ³ f ECM ç šw v Ê o á ³o 13 ò VDRAS o fñð ù¾ äž äž³ ù 13a ³ ( > 35 mm 2hr ) hpîpî³ -1 ä pî ³p¾ pî ³ u x³ Œyw³ VDRAS w f³ ùšõ ˆäž eò xpxw º w x oˆ f ì ³x 13 ³

25 ù v ò f 10 3 s 1 ºÁ -1.0 Ð-0.2 0.2 s Ñ v f ù 1 ÃoÆá 500 p Æ NW SE 9.

9 h ô 277 NW SE 8. CTL o fˆ Å 120 Ð 121 v Æ 22 Ð 23.2 vá (a) v 2.75 ù š 30 Ð 1 50dBZ 10 dbz Ñd õv Æ -0.6 Ð 1.5 ms ms oæ Æ ì (b) 0.25 ù v ò f 10 3 s 1 ºÁ -1.0 Ð s Ñ v f ù 1 ÃoÆá 500 p Æ NW SE 9. CTL o fš NW-SE d ³ š x 30 Ð 50dBZ 5dBZ ÑŸv f Æ K 0.2K t åˆæ ³ Ê åà ˆ áºo Î¼ ˆ

10 278 æ p 10. CTL o ˆ(a) f 60 ÿ 1254UTC (b) f 120 ÿ 1354UTC Š h š xá š Ð 30 Ð 50dBZ 10dBZ NCP o ³ få ECM o ³ få

11 h ô VDRAS ÍŠf vo Ño äž ošä½ (a)ð ù¾äž (b)ctl (c) NCP (d) ECM xá ù (mm) º i º Ã Æ œ p Æ šwv ç VDRAS o fšõ pˆ {x nù Åå ù Š Ð 1154 UTC j oš ån mm Å ³ CTL o ETS x mm Šò ECM h ³ Ë Ì NCP ETS ³ Ñ o Œ tï fá ç CTL o i ECM o x NCP o õõ o ³ ETS îõ 0.23 VDRAS WRF } «VDRAS õá fh Œÿˆ VDRAS xå Î ½ æ VDRAS f Ñ WRF ffå xùˆ j WRF w Ú WRF w³ xå Î òn ³y ˆ ˆš ƒå õ w f³ Îz

12 280 æ p òâ ³ˆw ½ x³wô pœä åà 14. VDRAS ÍŠf vo CTL NCP ECM ošä½ ETS ( ) }i¾ Œ ½ WRF w Œ Æ åå Arakawa C d ýæ d v ï x³ v Œh½y õ¼ ö ò VDRAS w Æ åå Arakawa C d ýæ d v v v šœ xå Î pˆš y õ¼³ Kessler scheme Kessler, 1969 õáå o Ë³ìˆù õá éˆåà ä o VDRAS ìˆ Êˆ WRF v i 15 º vˆ w³ ³Æ øê tªæ õá Š ç WRF w ÄÆ d02 Á ÅÆv ˆ å v Ñ VDRAS å n n vìˆ ³Å d ˆ WRF w ù ìå o ù õv 9.81 m s -1 x v t VDRAS w p v v³{ x ovˆ š least square fitting nd ˆ ³ ƒ õ ˆ x ³ x ³ åà õ 15. WRF wtªæ Á d01 d02 å v 6 ùñ 2 ù Æ d02 Ñ VDRAS Á åå ÅõÅ éˆ³žæ Å f u v w Ÿ θʹ q v q r q c éˆ ˆ VDRAS y éˆ WRF w ö òã y õ¼ éˆ Å y éˆš Å éˆ o éˆ Œ å ö WRF w ff Ãnx Å x fá Š ö VDRAS fñ WRF f ftïõh õ wå Š Ü vÿ³ ë o WRF ffñ VDRAS f ï õäžá Å ³ ù ï õ 100 ù ìˆ Ð VDRAS féˆ WRF féˆf ù 0 ù Ë 16 Æ VDRAS féˆ ù Ñ WRF féˆf ù y ˆ 1 Ì Å x éˆf ˆ WRF

13 h ô 281 d02 ëf ù f oš ¼ Ë Å ù x WRF féˆf ù Æ VDRAS f ù Ñ WRF féˆf ù o ˆ 1 î ô õ VDRAS f ùîh ( ) î Œ WRF o j Š ³ 2008 v 6 Œ 14 ³ 0600 UTC Æ ½ Á ³ ptªæ 15 d01 Ñ d02 vå v 6 ùñ 2 ù d02 Ñ VDRAS Á (two-way interaction) ö ìù ö Ä½ ³ d ˆ ƒ ô ³ σ v 46 p y ˆ ƒ Lin et al. (1983) ö p ˆ YSU scheme õá Ä WRF f o NCEP Global Final Analyses ECMWF Atmospheric Model ìˆ ˆ 0600 UTC j WRF w f Ñ VDRAS få o äo Á VDRAS Í Šf vžæo x ³ ½ f CTL NCP Ñ ECM Ñ WRF ½ 1200 UTC ffå x õá f Ç ç ˆŒ Ä WRF f o o ½ VDRAS f Å ½ WRF ff³ o Œ Äo ( )» Ä Ñ WRF Å ³ fo ³ ùvÿˆwo CTL o VDRAS ¾Ñƒ½åÀÍŠf³åå fñ WRF ffå ³ ³ Ë éˆf tïõhì µo³ š õu x ä³ o ÄÅ x õ f ˆ õ ö æ ç VDRAS ³éˆ ˆši A VDRAS = AVDRAS + AWRF AVDRAS (5) A VDRAS VDRAS ³ A éˆ A WRF Ñ AVDRAS WRF Ñ VDRAS éˆ A ³ vv xx ˆ³ A Åõõ ³ VDRAS Œ: A = A (6) VDRAS WRF ˆ³éˆ Aʹ vv ˆ WRF ³ vv õ½ˆw x ³¹ žo µo š³ õ Ê WRF x xx f³ ù šœh ³ Œˆ æäåçõäo ³Å i ETS äõ Ä wå ³o ³ o ECM o VDRAS ECMWF ìˆåàíšf³åå ³ f Ñ ECMWF ìˆ j ³ WRF ff Å f o á ƒ o o VDRAS+WRF Ñ Á ³ ECM o ECMWF ÍŠf VDRAS

VDRAS f px ò WRF VDRAS+WRF w f h º w xå Î oˆ f õ³t nù õáåç 18Ç WRF VDRAS (ECM)Ñ VDRAS+WRF o f ošäå ³ ETS ˆ o îä 18 Ç Á ž¾ñƒ½w ÍŠf³Ä Ã VDRAS o Å ( VDRAS

14 282 f³åå Œ Á ù ˆ VDRAS (ECM) ECMWF ìˆ jf³ä Ã WRF fo WRF õáòæä ³ ò äo 1200UTC Ð 1400UTC ošäå 17 WRF o h ˆ Ë Ñäž 13a ³ ä e Çp p Ç ò o çò h äž³ ù æ p 13a ä ³ hpî p¾³ y šœâ Å VDRAS fxõá f³ VDRAS + WRF o õìˆ Œ ˆƒ ƒ äæo ÄÅ³ ÊÑ¼ ˆ ù òƒôäžå hpîä ¾ ³ Œâ WRF o uýç³ Œ ³ h 17 õ ÄŒ WRF w³ få iñ Á VDRAS f Å 13b 13c 13d ò ³ VDRAS f px ò WRF VDRAS+WRF w f h º w xå Î oˆ f õ³t nù õáåç 18Ç WRF VDRAS (ECM)Ñ VDRAS+WRF o f ošäå ³ ETS ˆ o îä 18 Ç Á ž¾ñƒ½w ÍŠf³Ä Ã VDRAS o Å ( VDRAS (CTL)) õõäo o ³ fœò ³x oœ ³ š šœƒƒi i 10b 17. (a) WRFÑ(b) VDRAS+WRF ošä½ xá ù mm º i º Ã Æáœ p v 18. WRF VDRAS CTL VDRAS ECM Ñ VDRAS+WRF ošä½ ETS ˆ ò

15 h ô 283 e t ý Œ ½ o VDRAS ~ Ð xã WRF tï h i 6mm (10mm) ³ f äž ˆ œ³ o õ ˆŒ ³ ½³Å º ò x ³ VDRAS+WRF Œ x ˆ n ½o Ñ Š VDRAS+WRF o 0.27 (0.23) VDRAS (CTL)o 0.22 (0.19) VDRAS (ECM)o 0.14 (0.15) WRF o Œ (0.003) õ ³Å áº Å VDRAS fñ WRF o w Œ f³î «VDRAS WRF Œhuv³ƒ h ƒ³ WRF xå Î oˆp ž¾ f³x ¼v ùoœ 500 p Ð ù¾ Ä 54 ¾ åà ETS 19 Å º VDRAS+WRF Ñ VDRAS(ECM) Œ ³tïö h è WRF xå Î ¹ooˆ p få Œ u x Œ VDRAS ÓÎ Œ xå ³Î ~ Îhƒ r f³î ŒŒ f³œ t 19. VDRAS ECM Ñ VDRAS+WRF ˆœ 500 p ž¾ ošä½ ETS ˆ ò Äi : (1) VDRAS õìˆx ³ Ñõ få ³ pu f³ Ñ pî³î Ðz õ º VDRAS Œ x ~³ (2) ÍŠfoˆ VDRAS ³ Ñ f¹oœx h³x o ¾Ñƒ½äžìˆ ³ CTL o šœœ h ³äžì ˆ o ˆ ³ äæo Œ òi ³ƒƒ x fá ECMWF åàíšf³o (ECM) ˆ r f¼yõò oˆ ˆœ Æªo ³ f oò CTL ƒç ÓÎo ½ìˆ õ Å Ì³ÍŠf Î VDRAS Œ i³ få xè³t ù (3) Ú VDRAS Ä õäž ³ opv o ÄÅ³ìå Å WRF ³ xå Î õ ³ƒ f³á õ ò «VDRAS WRF f ETS ˆ³Œx ³õ (4) Œ ½ v õ Í g ìˆ iõo ÄÅÁ õˆ hš o ˆ õäž³ï Ð šn ÄÅ³ Ñ Å Šx få Œ æ ½ñwÊ šr Å r n õäžìˆ oˆ ž f

16 284 æ Ño ÄÅ³Å Œ i³ få (5) ùo h ÄÅ³ ½ i x o Œ ˆ å VDRAS ³ ÑÎ h VDRAS Å WRF ˆš ŒÓv õ i o VDRAS fñ WRF ff é ç ³ˆwõ áå (6) Œ ½ õá VDRAS Ñ WRF Å Š ŒË}i o x³wô Ä å À Œ ùo õ ³ (7) ƒo VDRAS g y õ¼ñ x å Î Œ ³ ½ˆ (8) Œ ½ ³ Œ n Œ fñäž ³ e õáìˆ Ñ fš³ Ë ë è ožëoì³ ä xœˆ o n VDRAS ÄÅ NCAR Dr. Juanzhen Sun ƒ è Ðè Œˆ o Ë ww ˆ 2010 vå NCAR Šn Œˆ Ë è Ñ SoWMEX hfo ³Œ î á³â Œ ½ ¼ å NSC M NSC M Ñ õ h ëp MOTC-CWB-99-2M-02 ì i Barnes, S. L., 1973: Mesoscale objective map analysis using weighted time series p observations. NOAA Tech. Memo. Erl Nssl-62, 60pp. Crook, N. A., and J. Sun, 2002: Assimilating radar, surface and profiler data for the Sydney 2000 forecast demonstration project. J. Oceanic Technol., 19, Atmos. Crook, N. A., and J. Sun, 2004: Analysis and Forecasting of the Low-Level Wind during the Sydney 2000 Forecast Demonstration Project. Wea. Forecasting, 19, Dowell, C. D., F., Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, Hu, M., M. Xue, J. Gao, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D level-ii data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, Kawabata J., H. Seko, K. Saito, T. Kuroda, K. Tamiya, T. Tsuyuki, Wakazuki, 2007: An assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensiojnal variational data assimilation system, J. Meteor. Soc. Japan, 85, Kessler, E., 1969: On the distribution and continuity of water substance in atmospheric circulation, Meteor. Monogr., 32, Amer. Meteor. Soc., 84 pp.

17 h ô 285 Lin, Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model, J. Appl. Meteor., 22, Schaefer, J. T., 1990: The critical success index as an indicator of warning skill, Wea. Forecasting, 5, Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon.Wea. Rev., 131, Sun, J., 2005: Initialization and numerical forecasting of a supercell storm observed during STEPS. Mon. Wea. Rev., 133, , M. Chen, and Y. Wang, 2010: A frequent-updating analysis system based on radar, surface, and mesocale model data for the Beijing 2008 Forecast Demonstration Project, Wea. Forecasting, 25, , and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observation using a cloud model and its adjoint. Part I: Model development and simulated data experiments, J. Atmos. Sci., 54, , and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16, , and Y., Zhang, 2008: Analysis and prediction of a squall line observed during IHOP using multiple WSR-88D observations. Mon. Wea. Rev., 136, Warner T., and co-authors, 2007: The Pentagon shield field program - Toward critical infrastructure protection, Bull. Amer. Meteor. Soc., 88, No. 2, Xiao, Q., and J. Sun, 2007: Multiple radar data assimilation and short-range QPF of a squall line observed during IHOP_2002, Mon. Wea. Rev., 135, Xiao, Q., Y.-H. Kuo, J. Sun, W.-C. Lee, E. Lim, Y. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44,

18 286 æ p Using Four Dimensional Variational Method and Doppler Radar Data to Improve Short Term Quantitative precipitation forecast a case study of a frontal system observed during 2008 SoWMEX field experiment Sheng-Lun Tai Yu-Chieng Liou Shao-Fan Chang Institute of Atmospheric Physics National Central University, Taiwan (manuscript received 8 October 2010 in final form 23 December 2010) ABSTRACT The Variational Doppler Radar Analysis System (VDRAS), developed by National Center for Atmospheric Research (NCAR), had been used to analyze low-level wind and convergence field in order to forecast thunderstorms at a real-time base. It was also applied to predict the evolution of supercells or squall line systems by assimilating multiple Doppler radar data. However, those studies were mostly performed over a wide open plain. In this research, it is for the first time that VDRAS is applied in the Taiwan and vicinity area. Since the complex terrain and limited observations due to the surrounding oceans pose great challenges, it is attempted in this research to find an appropriate strategy for using VDRAS under such conditions. A real case observed during IOP8 (Intensive Observation Period 8) of SoWMEX (Southwest Monsoon Experiment) on 14, June, 2008 is selected. VDRAS uses the data collected by radiosondes, surface stations, and re-analysis data to construct a background field, followed by assimilating the radial winds and reflectivity detected by two Central Weather Bureau S-band Doppler radars (RCCG and RCKT). Through Four-dimensional variational (4DVAR) adjustment, one obtains an optimal initial field, from which the VDRAS starts to make forecast. In addition, for a proper treatment of the influences from the complex terrain, it is also attempted to combine the VDRAS analysis field with WRF, and let the latter continue the forecast. It is found that VDRAS is able to retrieve reasonable kinematic the thermodynamic fields. The low layer convergence is consistent with the orientation of the local mountains, which implies that it is possible for VDRAS to reflect the topographic effects of the terrain. In terms of model forecast, VDRAS can correctly

19 h ô 287 capture the movement of the major precipitation system. The Equitable Threshold Scores (ETS) of predicted 2-hour accumulated rainfall are between 0.1 to 0.2. However, if VDRAS is merged with WRF, the resulting rainfall forecast skill can be significantly improved than that from using WRF or VDRAS alone. This research provides a possible alternative if VDRAS is to be applied in another region with similar geographic environment and observational limitations. Key Words: Four-dimensional Variational Method, Doppler radar data assimilation system

20 288 æ p

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