Evaluation of FY2E Reprocessed AMVs IN GRAPES. Wei Han, Xiaomin Wan and Jiandong Gong NWP/CMA
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1 Evaluation of FY2E Reprocessed AMVs IN GRAPES Wei Han, Xiaomin Wan and Jiandong Gong NWP/CMA IWW13, June 2016
2 Outline l FY2E reprocessed AMVs l Observation Error and Number l Experiments in GRAPES global forecast system l Conclusion and discussion Slide 2
3 Reprocessed FY2E AMVs: algorithm changes l A new calibration system l Preprocess of satellite image - Eliminate the influence of abnormal - Remove noise in the satellite image l Second tracking algorithm l Height assignment the slow biases are reduced 1 st : 32 X 32 2 nd :16 X 16 - inversion layer - target box full of cloud Slide 3 Zhang Xiaohu et al.,2014: Status of operational AMVs from FY-2 satellites since the 11th winds workshop, IWW12.
4 hPa hPa hPa Slide 4
5 201308: IR winds Slide 5
6 O-A(ERA_INTERIM) l ~ RMSE BIAS Slide 6 O-A(U wind, unit: m/s)
7 ü Regional 3DVar (P- Level) ü Unified dynamic frame ü Regional physics package ü Initiation of GPAPES Programme ü Sponsored by the MOST and CMA üoperation of GRAPES-GFS V2.0 On June 1 st 2016 üoperation of GRAPES-Meso V1.0 üprototype of GRAPES-GFS ü Regional 3DVar (M- Level) ü Pre-operation of GRAPES-GFS V1.0 üupgrade of GRAPES-Meso V3.0 Milestone of GRAPES ü Operation of GRAPES-RAFS ü Prototype of Regional Slide 4DVar üoperation of GRAPES-TYM üupgrade of GRAPES-Meso V4.0 üupgrade of GRAPES-GFS V1.4
8 GRAPES global forecat system: performance T639: CMA operational global system GRAPES V1.4 (2013) GRAPES V2.0 (2015) N.H. 500hPa N.H. Day-5 ACC at 500hPa timeseries Slide 8
9 Main features of GRAPES 3DVar GRAPES 3DVar Model-level: M3DVar Press-lev: P3DVar Height-based terrain following Pressure coordinate Vertical coordinate coordinate & Charney-Phillips staggered grid Horizontal grid Arakawa-C Arakawa-A Analysis variable π,u,v,q/rh/rh*/normalized rh* φ,u,v,rh/q Background error covariance Control variable transformation Balance constraint Horizontal and vertical correlation separated/non-separated, NCEP(NMC) method for error co-variances Horizontal filter and vertical EOFtransformation Linear balanced + pressure-wind balance statistic regression Spatial interpolation Bi-linear interpolation (horizontal), linear or 3 rd spline interpolation (vertical) Minimization Limited memory BFGS method Program design Modular and parallel design Slide 9
10 Data Assimilation Experiments in GRAPES l GRAPES global forecast system - LAT/LON grids, Terrain-following Height - 3D-Var, 0.25*0.25degree, L60(model top=3hpa) - Sonde, Synop, GPS_RO, AMSU-A, IASI, AIRS, MWHS2(FY3C) - Become operational on June 1 st 2016 l Experiments(0.5*0.5 degree) - CNTL: All+FY2E AMVs(old) - EXP: All+FY2E AMVs(new) Slide 10
11 Assessment and Assimilation of high latitude Geo_Leo winds in GRAPES GEO+LEO+GEO_LEO winds Slide 11 Thanks Dr. Matthew A. Lazzara, Dr. David Santek,Dr. Brett Hoover at SSEC! for providing Leo_Geo Winds
12 Bias(O-B) time series Slide 12
13 <O-B> of FY2E old and new AMVs(IR), hPa-100hPa 700hPa-400hPa FY2E NEW FY2E OLD Slide hPa-700hPa
14 Geometrical interpretation of analysis l Practical Implementation - Multi. Variable and Obs. - QC l Monitoring of Obs. Error - based ono-b and O-A o 2 o o ( ) = > 0 a b d d - Easy to use The diag. of R: o ( ) o o d T a d b 2 o = a E[ ( )] = R d d o b Slide 14 Desroziers, G. et.al,diagnosis of observation, background and analysis-error statistics in observation space, Q. J. R. Meteorol. Soc. (2005), 131, pp
15 Observation Error Estimation using Desroziers s method FY2E NEW Diagnosed Obs. Error FY2E OLD Obs. Number(used) FY2E NEW FY2E OLD Slide 15
16 Verification of wind analyses against NCEP global analysis: RMSE time series at 150hPa Slide 16
17 Verification of wind analyses against NCEP global analysis: RMSE vertical profile Slide 17
18 Impact on Wind forecasts in East Asia Slide 18
19 Impact on Wind forecasts N.H Tropics S.H Slide 19 Global
20 Impact on forecasts S.H. Slide 20
21 Conclusions and Discussions l The reprocessed FY2E IR AMVs - Observation number Increased (about 3 times ) - The mean biases are reduced - The diagnosed observation error reduced - The impact on GRAPES analyses and forecasts is Positive l Ongoing work - Tuning of the observation error of the AMVs - Height adjustment of AMVs in DA Slide 21 - Assimilation of Hourly AMVs in GRAPES Global 4D-Var - Use of the reprocessed FY2 AMVs in China Reanalysis
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