Implementation and Evaluation of WSR-88D Radial Velocity Data Assimilation for WRF-NMM via GSI

Transcription

1 Implementation and Evaluation of WSR-88D Radial Velocity Data Assimilation for WRF-NMM via GSI Shun Liu 1, Ming Xue 1,2 1 Center for Analysis and Prediction of Storms and 2 School of Meteorology University of Oklahoma, Norman OK

2 Introduction and Motivation Full volume data from the entire 88D network have become available at NCEP recently that can be used for NWP model initialization. Due to the presence of quality problems of radar data, quality control (QC) is essential. The huge volume of radar data currently prevents the use of all radar data in the operational data assimilation system. The relatively low resolutions of the current operational models, as compared to the native resolution of radar data, also make the assimilation of full-volume data unnecessary. Radar radial velocity is a special form of observation that is neither scalar nor vector; special treatment is therefore needed.

3 Radar radial wind QC The radar velocity quality control (QC) package currently in use at NCEP was developed at NSSL (Xu et al. 2004; Liu et al. 2003; Gong et al. 2004; Zhang et al and Liu et al. 2005). The package contains 4 components: (1) radial velocity dealiasing; (2) ground clutter removal; (3) migrating bird detection; (4) noisy data removal.

4 Radar radial wind QC At NCEP, data from 120 radars are processed by one 8 CPUs node IBM machine. Computation efficiency is very important One of my task during my visit to NCEP was to integrate radar wind QC package into real-time data flow: (1) redesign and modify the interface of NSSL QC package (2) test QC package under operational computation environments at NCEP. (3) Optimize the code structure of the QC package based on the operational needs. With code optimizations, the average time of processing a volume radar data is reduced from 25.6 s to 9.8 s.

5 The super-obbing Technique super-obbing technique is developed (Purser et al. 2000) recently for reducing redundant information in observational data as well as in reducing data density. super-obbing technique is used to preprocess the Level-II WSR-88D radar data (Parrish 2005) for thinning and combining radar radial velocity data. The super-obbed data are then analyzed in the NCEP unified GSI (Grid-point Statistical Interpolation) analysis system. The impact of the super-obbed data on the analysis and forecast is examined in this study.

6 Radar data super-obbing In Parrish (2005), the radar radial velocity is given by v ( x) = ud + vd + wd r x y z where x = (x,( y, z) ), (d x, d y, d z ) = [(x x r )/r d, (y( y r )/r d, (z( z r )/r d ] are direction cosines. r d = [(x x r )2, (y( y r )2, (z( z r )2]1/2 is the radial distance from radar, (x r, y r, z r ) = radar position

7 Radar data super-obbing The projection of the radial velocity field in the observation space is a vector defined by y = [ v ( x ) m = 1, 2,... M], r m y = Ha, H x y z d ( x1) d ( x1) d ( x1) x y z d ( M) d ( M) d ( M) x x x Radar obs Super obs a = (u, v, w) T

8 Radar data super-obbing Gram-Schmidt algorithm is applied to decompose H, H = GH * G: M X 3 H*: 3 x 3 Minimizing (Ha y obs )R -1 (Ha y obs ) y * = H*a. where y* = (G T R -1 G) -1 G T R -1 y obs y * is the superob derived from y obs Thus, the superob reduces M pieces of information to 3.

9 The Grid-point Statistical Interpolation (GSI) System GSI (Wu et al. 2002) is a grid point version of the NCEP Statistical Interpolation (Parrish and Derber 1992) It is based on a 3D variational (3DVAR) algorithm. The cost function is defined by J = + H H T 1 1 1/2[ xb x ( x y) R ( x y)] B is modeled using a recursive filter (Purser et al. 2003a; 2003b). Variable transformation and preconditioning are also performed (Wu et al. 2002). The filter scales used by the recursive filter will impact analysis result.

10 A Real Case Study the precipitation case of 23 May 2005 Mesoscale convective system with precipitation located in Oklahoma, Kansas and Missouri, on 23 May 2005 Was observed by five WSR-88D radars: KTLX (Oklahoma city, OK), KINX (Tulsa, OK), KSRX (Fort Smith, AR), KSGF (SpringField, MO) and KVNX (Vance AFB, OK). On that day, two convective cells formed near the Kansas-Oklahoma boundary, along a frontal zone. They developed into a mesoscale convective system with precipitation near 0900 UTC.

11 Radar reflectivity at 0900 UTC on 23 May 2005

12 A crude mosaic of radial velocity at 0900 UTC on 23 May 2005 from the five radars

13 The experimental GSI analysis of near surface winds without radar data at 0900 UTC on 23 May 2005

14 Impact of background error decorrelation length The default decorrelation length of the background errors used in GSI is estimated using the NMC method. It may not be suitable for analyzing radar data representing convective scales. Experiments with decorrelation lengths that are 0.25, 0.5 and 2 times the default value are performed. All observations from 5 radars are superobbed to a regular grid with a 0.1 o resolution in the horizontal and a 500 m resolution in the vertical. All use the same WRF-NMM analysis shown earlier as the background

15 Analysis using default decorrelation length Increment of the analyzed winds Full wind vectors

16 Analysis using 0.25 times the default decorrelation length Increment of the analyzed winds Full wind vectors

17 Impact of super-ob grid resolution In this set of experiments, the vertical resolution is fixed at 500 m and the horizontal resolution is set to 0.05, 0.1, 0.25 and 0.5 o. Analyses are performed using a reduced decorrelation length (by a factor of 0.25) and the same background as used earlier. Results from two experiments that use super-ob horizontal resolutions of 0.05 o and 0.5 o are presented here.

18 Analysis using super-ob resolution of 0.5 in the horizontal Increment of the analyzed winds Full wind vectors

19 Analysis using super-ob resolution of 0.1 in the horizontal Increment of the analyzed winds Full wind vectors

20 Analysis using super-ob resolution of 0.05 in the horizontal Increment of the analyzed winds Full wind vectors

21 Impact of radar wind on WRF-NMM 6-h forecast The impact of decorrelation length and super-ob grid resolution of radar radial wind on WRF-NMM short-term forecast is further examined using May 23 case. A horizontal resolution of 8 km was used over operational WRF-NMM central domain. Other parameter setting is the same as NCEP operational WRF-NMM. 6-h forecasts starting from GSI analysis at 0600 UTC were performed. GSI analyzed fields without assimilating radial wind were used as background to further analyze radar radial winds.

22 GSI default decorrelation length Superob resolution 0.5 deg Decorrelation length is 1/8*default one Superob scale 0.1 deg Vector wind Analysis Increment Total vector wind field

23 True radar wind observation GSI default filter scale Superob scale 0.5 deg GSI default filter scale Superob scale 0.5 deg Vector wind Analysis Increment Total vector wind field GSI analyzed wind increment and vector wind field

24 True radar wind observation Radial wind from KTLX near 1200 UTC Vector wind Analysis Increment Filter scale is 1/8*default one Superob scale 0.1 deg Filter scale is 1/8*default one Superob scale 0.1 deg Total vector wind field GSI analyzed wind increment and vector wind field

25 Composite reflectivity from 5 radars With small filter and super ob scale the forecast reflectivity is improved. GSI default filter scale Superob scale 0.5 deg Filter scale is 1/8*default one Superob scale 0.1 deg 6-h forecast

26 6h-forecast fits to observations Psfc uv t q Without Radar wind Default decorrelation length & 0.5 o super-ob Reduced decorrelationlength & 0.1 o super-ob Psfc: surfcace pressure (mb) Uv: wind field (m/s) T: temparature ( K ) Q; relative-humidity (%) % improvments in uv and q

27 Summary The radar radial wind QC package is integrated into NCEP real-time radar data processing procedures. The NCEP GSI 3DVAR system is used to analyze super-obbed radial velocity data from 5 WSR-88D radars for a mesoscale convective system that occurred on 23 May 2005 near the Kansas- Oklahoma boundary. The experimental GSI analysis on an 8 km WRF-NMM grid without radar data was used as the background. radar data are analyzed in the second pass. other data analyzed in the first pass We are therefore using a multi-pass analysis procedure The impacts of the error decorrelation length of the background error and the radar data super-obbing size or resolution on analysis and 6-h forecast are examined.

28 Conclusions The default decorrelation length scale is too large for the analysis of radar data. It causes an unreasonably smooth analysis, with grid points far away from the radar observations being incorrectly influenced by the data. The strong smoothing causes the loss of convective scale structures in the analysis and results in a too weak a convergence line As the decorrelation length scale decreases, the convergence near the wind shift line becomes stronger and the overall structure of wind analysis appears to be significantly improved. With a more suitable decorrelation length scale, increasing super-ob grid resolution further improves the analysis. More detailed flow structures are obtained. Decreasing the super-ob grid resolution results in the loss of the smallscale details contained in the original radar observations. With reduced decorrelation length and high-resolution of super-ob, 6-h forecast is improved for this case.

29 Future Plan Examine a multi-pass analysis strategy for dealing with radar data in GSI with more cases or over a period, such as a week. Examine the impact of radar data assimilation cycle on WRF-NMM forecast. Examine the impact of radar wind QC on GSI analysis and WRF-NMM forecast Estimate the most suitable decorrelation lengths of radar data and apply anisotropic background error covariance

30 Thanks are due to DTC for providing me the opportunity for visiting NCEP. Drs. Stephen Lord, Geoff DiMego, David Parrish, Wan-shu Wu, Huiya Chuang, Krishna Kumur, Russ Treadon, Eric Rodger and Matthew Pyle for their help during my visit to NCEP. Drs. Qin Xu and Pengfei Zhang for their collaborative work and help, and discussions about radar radial wind QC. Dr. Jidong Gao for discussions on variational radar wind assimilation.

31 References Parrish, D. F. 2005: Assimilation strategy for level 2 radar winds. Presentation, 1st GSI User Orientation. Camp spring, MD [available online at htpp://wwwt.emc.ncep.noaa.gov/gmb/treadon/gsi/documents/presentations/1st_ gsi_orientation]. Purser, R. J., D. Parrish and M. Masutani, 2000: Meteorological observational data compression; an alternative to conventional "super-obbing". Office Note 430, National Centers for Environmental Prediction, Camp spring, MD [available online at htpp://wwwt.emc.ncep.noaa.gov/officenotes/fulltoc.html#2000] Wu, W.-S 2005: Background error and their estimation. Presentation, 1st GSI User Orientation. Camp spring, MD [available online at htpp://wwwt.emc.ncep.noaa.gov/gmb/treadon/gsi/documents/presentations/1st_ gsi_orientation]. Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130,

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