Retrieval of Moisture from GPS Slant-path Water Vapor Observations using 3DVAR and its Impact on the Prediction of Convective Initiation and Precipitation EMC seminar 04/17/2007 Haixia Liu 1,2 and Ming Xue 2 1 NCEP/EMC 2 SoM and CAPS, University of Oklahoma

 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) Introduction

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

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

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

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.

The flow-dependent B is formulated directly in terms of the error field given a physically meaningful correlation function form. Flow-dependent Anisotropic B 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. The flow-independent B is often assumed to be Gaussian:

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

GPS Observation System Control segmentGround-based receiverSpace segment

. The GPS-Met network consists of 386 sites. Ground-based GPS Network

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

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

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

Analysis background

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

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

List of Analysis Experiments Experimentanisotropic Filter RMSE (g kg -1 )CCCC with EF ISO_RFNo * ANISO_RFYes UB_RFYes * 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.

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

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

Sensitivity to L r & L f RMSE (g/kg) w.r.t. L r ISO is the worst ISO is more sensitive to L r UB with different L f is in between ANISO is the best (impossible for practical application) optimal L r s

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.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.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.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.Isotropic analysis is more sensitive to geometric de-correlation scale, Lr, than anisotropic analysis. (Results reported in Liu and Xue 2006; Liu et al MWR)