Z-I A b Z-I A b Z Z-I A A b Z-I Miller [5] Z i I i Z-I [6] Z-I Z-I Z-I Z-I Z I Wilson [7] 1970 [8] [9] 20.32% Sasaki [10] Nino- miya [11] [12]

Size: px

Start display at page:

Download "Z-I A b Z-I A b Z Z-I A A b Z-I Miller [5] Z i I i Z-I [6] Z-I Z-I Z-I Z-I Z I Wilson [7] 1970 [8] [9] 20.32% Sasaki [10] Nino- miya [11] [12]"

磐籍
7 years ago
Views:

1 24 2 Vol.24, No JOURNAL OF TROPICAL METEOROLOGY Apr., (2008) ( ) Z-I A b Z I (Haitang) (Matsa) Z-I Z-I P A 1 [1] [2] [3] 36 Z-I (Z=A I b Z I A b ) Z-I A b Z-I [4] Z I Z-I : ; (2006C13025) (CMATG2006Z11) 01 (2004CB418301) ( ) : jichunxiao@sina.com

2 Z-I A b Z-I A b Z Z-I A A b Z-I Miller [5] Z i I i Z-I [6] Z-I Z-I Z-I Z-I Z I Wilson [7] 1970 [8] [9] 20.32% Sasaki [10] Nino- miya [11] [12] 100% 200% 20% 30% [13] 56% 9.9% [14] [15] Z-I A b (Haitang) (Matsa) 1 h( 10 ) 1 h mm mm

3 Z-I 3.1 CINRAD-SA Z-I ( Z=300 I 1.4 ) Z-I Z-I Z-I Z-I Z-I Z= A I b Z I Z Z-I Z-I b 5 A b Z-I (1) ( ) Z-I Z 1 > Z 2 > Z 3 > > Z n > I 1 > I 2 > I 3 > > I n > ( L L=8) [Z min Z max ] [Z 1 Z l ] [Z 2 Z 1+l ] [Z n l+1 Z n ] I [I 1 I l ] [I 2 I 1+l ] [I n l+1 I n ] Z k I k A b (2) A b Z-I I Z k Z k Z n Z k I n Z I A b 3.2 Z-I Z I Z-I Z-I Z-I Z-I Z-I Z-I Z-I Z-I 3.3 Z-I Haitang Matsa Z-I A b ( 1) 1 A b A b A b 1 Z-I A b /UTC A b Haitang Matsa

4 A b Z-I 1 Z-I A b 4 Z-I - R(i j) R g (i j) (i j) [11] CR (i j)= R g (i j) R(i j) CR (i j) CR(i j) J *(1) *(1) J = α( CR CR ) + ε[( CR) + ( CR) ] i j x y min (1) Euler 2 2 ( ) [ α CR CR ε ( CR) + ( CR) ] = 0 (2) 2 2 x y CR(i j) R*(i j)=cr(i j)+r(i j) (3) Haitang 5 mm 1a (UTC ) 45 dbz 20 mm 78 mm 76 mm 45 dbz ( 1b) Haitang 45 dbz 50 mm 45 dbz 2a 1 Z-I A b ( ) ( ) ( ) Haitang 2a 5 mm 70 mm 78 mm 60 mm 76 mm 28.5 N E 20 mm 2b 2b 5 mm 80 mm 20 mm 20 mm 3 Haitang ( ) ( ) 1b Haitang a

5 mm 50 mm 78 mm 1 Haitang (dbz ) (mm ) a b Haitang (mm ) (mm ) a b Haitang ( ) (mm )

6 mm 50 mm 28.0 N E 23 mm 30 mm 27.3 N E 3b 45 mm 28.0 N E 30 mm 27.3 N E Matsa ( 4a) Matsa 40 dbz 35 dbz 5 mm 37 mm 45 dbz ( 4b) Matsa 4 h Matsa 32 mm Z-I Matsa A b 5 Matsa ( ) ( ) 5a 20 mm 33 mm 37.2 mm 5 mm ( 5b) 20 mm 33 mm 35 mm 6 Matsa ( ) ( ) 6a 10 mm 27 mm 32.4 mm ( 6b) 30 mm Haitang ( 7a) s 1 Matsa ( 7b) s 1 28 N E 10 mm

7 mm 32 mm 4 Matsa (dbz ) (mm ) a b Matsa ( ) (mm ) a b Matsa ( ) (mm )

8 (mm ) 2 ( 10 4 s 1 ) 6 30% 2 2 /UTC /mm /mm /mm /mm /mm /% /% / / / R gi R i R g 1 N N i = 1 = R (4) gi N 1 E = R R 100% (5) r i gi N R g i = 1 1 N ar = i gi N i = 1 E R R (6) Haitang Matsa ( 2) 2 Matsa % 11.05% 7 Haitang Matsa Z-I A b (1) Z (2) Z-I

9 2 155 (3) (4) (5) [1] [J] (1) 1-9 [2] [J] (5) [3] CINRAD WSR-98D [J] 2006, 22(6) [4] [M] [5] MILLER J R A climatological Z-R relationship for convective storms in the northern Great Plains Preprints[C]//15th radar meteor conference Boston AMS [6] CINRAD [J] (1) [7] WILSON J W. Radar Measurement of rainfall summary[j] Bull Amer Soc (9) [8] [J] 1990, 13 (4) [9]. [J] (2) [10] SASAKI Y Some basic formulas in numerical variation analysis[j] Mon Wea Rev [11] NINOMIYA K, AKIYAMA T. Objective analysis of heavy rainfalls based on radar and gauge measurement[j] J Meteor Soc Japan [12], [J] (2) [13] [J] (2) [14] [J] (4) [15], [J] (10) 3-7 A STUDY ON VARIABLE QUANTITATIVE PRECIPITATION ESTIMATION USING DOPPLER RADAR DATA JI Chun-xiao 1, CHEN Lian-shou 2, XU Xiang-de 2, ZHAO Fang 3, WU Meng-chun 3 (1. Zhejiang Institute of Meteorological Sciences, Hangzhou , China; 2. Chinese Academy of Meteorological Sciences, Beijing , China; 3. Wenzhou Meteorological Bureau, Wenzhou , China) Abstract With the pros and cons of the traditional optimization and probability pairing methods considered, an improved optimal pairing window probability technique is developed using a dynamic relationship between the base reflectivity Z observed by radar and real time precipitation I by rain-gauge. Then, the Doppler Radar observations of base reflectivity for typhoons Haitang and Matsa in Wenzhou are employed to establish variable Z-I relationship which is subsequently used to estimate hourly precipitation of the two typhoons. Such estimations are calibrated by variational techniques. The results show that there exist significant differences in Z-I relationships for different typhoons, leading to different typhoon precipitation efficiencies. The estimated typhoon precipitation by applying radar base reflectivity is capable of exhibiting evidently the spiral rain belts and mesoscale cells, and well matches the observed rainfall. Error statistical analyses indicate that the estimated typhoon precipitation calibrated is better than the one without variational calibration. The variational calibration technique is able to maintain the characteristics of distribution of radar estimated typhoon precipitation, and to significantly reduce the error of the estimated precipitation in comparison with the observed rainfall. Key words: typhoon; radar quantitative precipitation estimation; variational calibration; verification

~4 197~ [8] [11].5.5 A grid V grid A r ϕ ϕ λ r ϕ 2 2 grid = cos = (π /36) cos V grid = A grid P grid 1 6 r ϕ A grid km 2

~4 197~ [8] [11].5.5 A grid V grid A r ϕ ϕ λ r ϕ 2 2 grid = cos = (π /36) cos V grid = A grid P grid 1 6 r ϕ A grid km 2 24 3 Vol.24, No.3 8 6 JOURNAL OF TROPICAL METEOROLOGY Jun., 8 :14-4965(8)3-233-6 1 2 2 2 1. 442. 181 7 mm 1 mm 4~12 8 1957~4 : :P444 :A 1 [1] 4 5 6 199 [23] [4] 198~199 [5] [6] Rodgers [7] 444 km [8] [91]