Haixia Liu 1,2 and Ming Xue 2 1 NCEP/EMC 2 SoM and CAPS, University of Oklahoma

1. Retrieval of Moisture from GPS Slant-path Water Vapor Observations using 3DVAR and its Impact on the Prediction of Convective Initiation and PrecipitationEMC seminar04/17/2007 Haixia Liu1,2 and Ming Xue2 1 NCEP/EMC 2 SoM and CAPS, University of Oklahoma

2. Introduction • Accurate characterization of 3D water vapor is important. • for the forecast of CI and subsequent storm evolution • for QPF • Water vapor is under-sampled for convection processes. • GPS can potentially provide water vapor measurements at high spatial and temporal resolutions under all weather conditions. • One form of GPS measurements is the slant-path water vapor (SWV) derived from slant-path total delay. • Because of the integrated nature of the SWV data, their analysis is non-trivial and require advanced methods. • This study develops a 3DVAR system for analyzing SWV data. • Examines the impact of SWV data on CI and QPF (preliminary results)

3. Outline • 3DVAR method • GPS observation system • Moisture retrieval from SWV data with spatial filters • Numerical simulation of 12 June, 2002 IHOP case • Impact of GPS data on CI and QPF within OSSE framework

4. A new control variable is defined as 3DVAR System with Explicit Filter Here, so as to exclude the inverse of B in the definition of J and to use explicit filter to replace B. (Liu and Xue 2006 MWR)

5. 3DVAR System with Recursive Filter (Liu, Xue, Purser and Parrish 2007 MWR)

6. Background Error Covariance B B is crucial to the successful analysis because: • variances determine the relative weights for the background and observations; • spatial covariance determine the spatial spreading or smoothing of observational information; • for multivariate analysis, cross-covariances reflect balance properties among fields.

7. Flow-dependent Anisotropic B The flow-independentB is often assumed to be Gaussian: • The flow-dependentB is formulated directly in terms of the error field • given a physically meaningful correlation function form. An important difference is the analysis background field is used as the f in his case. In our case, f, is defined as the error field.

8. This test is general – not related to SWV. Did it use EF or RF? Analysis increments from a single sfc observation Obs=14.72 g kg-1 Bg = 0 g kg-1 Ana=14.69 g kg-1 Dryline single sfc ob. Isotropic B Anisotropic B Lr = 4 grid intervals Lf = 2 g/kg

9. GPS Observation System Space segment Control segment Ground-based receiver

10. . Ground-based GPS Network The GPS-Met network consists of 386 sites. http://gpsmet.fsl.noaa.gov/jsp/index.jsp

11. Ground-based GPS Data Ionospheric delay Estimate from dual frequency observations Total atmospheric delay Hydrostatic delay Estimate from surface pressure measurements Neutral delay Wet Delay (SWD) pw: precipitable water in a column in vertical direction ZWD: zenith wet delay

12. 3D Moisture Retrieval/Analysis with 3DVAR from GPS SWV and Surface Station Data using Spatial Filters

13. GPS satellite (km) km GPS receiver km Observation System Simulation Hypothetical GPS Network truth give number of sat and ground station spacing qv field valid at 2000, 19 June, 2002

14. Analysis background

15. Explicit filter • Anisotropic B based on true error field truth-background analysis increment A B

16. A A B B Explicit Filter • ISO • Isotropic B analysis v.s. truth (solid) analysis increment • UB • (Updated B) • Anisotropic B • But the f field is the ISO analysis increment • This is a two-step iterative procedure

17. List of Analysis Experiments *Lr = 4, in unit of grid point, which is optimal, for ISO_RF experiment while Lr = 3 is optimal for ISO experiment using explicit filters.

18. B A Recursive Filter ISO_RF: Isotropic B CC=0.84; RMSE=0.35 g kg-1 analysis increment truth-background

19. B A B A Recursive Filter ANISO_RF: Anisotropic B based on truth CC=0.91; RMSE=0.28 g kg-1 analysis increment xz cross-section along AB UB_RF: (covariance-updated) Anisotropic B based on the analysis with isotropic B CC=0.86; RMSE=0.34 g kg-1

20. ISO is the worst ISO is more sensitive to Lr UB with different Lfis in between ANISO is the best (impossible for practical application) optimal Lrs Sensitivity to Lr & Lf RMSE (g/kg) w.r.t. Lr

21. Summary 1 • Our 3DVAR system incorporating background error through an isotropic Gaussian filter properly recovers 3D meso-scale moisture structure in a dryline case. • The use of flow-dependent background error covariances realized through an anisotropic spatial filter improves the analysis. • The two-step iterative procedure to estimate B proposed (covariance-updated) improves upon the result of isotropic analysis. • Compared to EF, the biggest advantage of RF is the computational efficiency. • The quality of analyses using RF is in general comparable to or better than those obtained with EF in terms of CC. • Isotropic analysis is more sensitive to geometric de-correlation scale, Lr , than anisotropic analysis. (Results reported in Liu and Xue 2006; Liu et al. 2007 MWR)