# Presentation: 442534

## Analysis and Impact of Moisture from GPS Slant-path Water Vapor Observations using 3DVAR with Flow-dependent Background Error EMC seminar04/17/2007

- Haixia Liu1,2 and Ming Xue2
- 1 NCEP/EMC
- 2 SoM and CAPS, University of Oklahoma

## Introduction

- 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 with possible high resolution under virtually all weather conditions.
- One form of GPS measurements is the slant-path water vapor (SWV).
- This study develops a 3DVAR system for analyzing SWV data.
- Examine the impact of SWV data on CI and precipitation (preliminary results)

## Outline

- 3DVAR system with flow-dependent background error
- Moisture retrieval from SWV and surface data with 3DVAR
- Numerical simulation of 12 June, 2002 IHOP case
- Impact of SWV data on predicting CI and precipitation within OSSE framework

## GPS Observation System

- Control segment

- Ground-based receiver

- Space segment

## Ground-based GPS Network

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

## Ground-based GPS Data

- Total atmospheric delay

- Ionospheric
- delay
- Estimate from dual frequency observations

- Neutral delay

- Hydrostatic
- delay
- Estimate from surface pressure measurements

- Wet
- Delay  (SWD)

- pw: precipitable water in a column in vertical direction
- ZWD: zenith wet delay

## 3DVAR System with EF

- so as to exclude the inverse of **B** in the definition of J and to use explicit filter to replace **B**.

- Here,

## 3DVAR System with RF

## 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.

## Flow-dependent Anisotropic B

- 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.

## Question:

-               How to obtain *f* which represents the background error pattern?
- Solution:
-               *f* = isotropic analysis increment

## 3D Moisture Retrieval from Simulated SWV and Surface Data with 3DVAR using Anisotropic Spatial Filters

## Observation System Simulation

**truth**

**Hypothetical GPS Network**

- receiver reso = 150 km

## Analysis background

**A**

**B**

## Explicit filter

- Anisotropic **B** based on true background error

- analysis increment

**A**

**B**

- truth-background

- Solid: truth
- Dashed: ana

## 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

- Explicit Filter

## Sensitivity to Lr & Lf

- Sensitivity to *L**r** & L**f*

- RMSE (g/kg) w.r.t. *L**r*

## 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.

**Notes:**
Don’t say NOT SHOWN

## Numerical Prediction of Convective Initiation of June 12, 2002 IHOP Case

- To obtain a realistic high-resolution simulation
- To understand the convective initiation processes (not reported here)
- To perform OSSEs on this case with 3DVAR system we developed in order to investigate the impact of SWV data on CI and subsequent storm evolution.

## Case Overview

- 12Z, 12 June – 03Z, 13 June, 2002

## 2045 UTC, 12 June 2002

## Model Configurations	

- ARPS model with full physics, including ice microphysics + soil model + PBL and TKE-SGS turbulence

**1200 UTC**

**1800 UTC**

**0300 UTC**

**CI ~ 2100UTC**

**0000 UTC**

**ADAS**

**ADAS**

- dx = 3km

**Notes:**
3km horizontal resolution
50 layers over 20km depth with dzmin=20m
12-18Z cycled analysis and initialized at 18Z, 9 hr forecast 
BC from ETA analyses at 12, 18, 06 and forecasts at 15, 21, 00, 03
Lin 3-ice microphysics
No cumulus parameterization
TKE-based PBL-mixing parameterization
1.5-order TKE-based subgrid turbulence parameterization
NASA GSFC long and short-wave radiation package

## timing and location of CIs;

- 1b

- 1a

- 1c

- 2

- 3

- cells split, regroup and intensify

## @2130, 12 June 2002

- radar observation            model forecast

## @0000, 13 June, 2002

- radar observation            model forecast

## @0100, 13 June, 2002

**Initiation of new cells by colliding boundaries 7 hours into the forecast**

- radar observation            model forecast

## Summary 2

- The 12 June, 2002 case involved initiations of many convective cells. It is a complicated case with many mesoscale features playing roles in CI.
- ARPS model with 3km horizontal resolution produces rather realistic simulation of this case. It successfully reproduces most of the observed convective cells with remarkable accuracy in initiation timing and location compared to the simulation from Bastin et al. (2005).
- Predicts well the general evolution of the precipitation within the first 7-hour forecast period, including cell splitting, merging, regrouping and the triggering of secondary convective cells by outflow boundaries colliding with each other.
- The main deficiency of the prediction is the lack of organization of cells into a squall line and its inaccurate propagation during the last 2-hour of 9-hour forecast.

## Impact on CI and Precipitation via OSSEs

- Experiment design:
-  SWV and surface data are generated from the numerical simulation.
-  Analyze data using isotropic and flow-dependent **B** respectively.
-  Analysis results are used as ICs to initialize ARPS model.
-  ETA analysis, serving as the analysis first guess, is used as IC to make a forecast.
-  Forecast results are verified against the ‘truth’ to investigate the impact of SWV data and the use of flow-dependent B on the forecast.

## Moisture field @ 1800

- truth

- guess

- ISO

- UB

## Slide 30

## EW vertical cross-section along AB

- truth

- guess

- ISO

- UB

## Impact of analysis on Precipitation

## 2045 UTC, 12 June 2002

## List of forecast experiments

| Forecast experiments | Experiment description |  | CI timing (UTC) |  |
| --- | --- | --- | --- | --- |
|  |  |  | CI2 | CI4 |
| Without GPS | Moisture initial condition from ETA analysis |  | 2040 | 2000 |
| withGPS_ANISO | Moisture initial condition from 3DVAR analysis using GPS SWV and surface moisture data | Anisotropic B based on true background error | 2040 | 2030 |
| withGPS_ISO |  | Isotropic B | 2040 | 2150 |
| withGPS_UB |  | Anisotropic B based on isotropic analysis | 2040 | 2040 |
| ‘Truth’ |  |  | 2040 | 2130 |

## Moisture Analysis

- truth

- guess

- ISO

- UB

## Analysis within Zoomed-in Region

**guess**

**Truth**

- 15g/kg

- 14g/kg

- 15g/kg

- 15g/kg

**c**

**d**

- 14g/kg

- 14g/kg

- 15g/kg

**ISO**

**UB**

**c**

## 2 hr Forecast

**fcst_noSWV**

**Truth**

- 13.5g/kg

- 15.5g/kg

- CI @ 2130

- CI @ 2150

- CI @ 2040

- 14g/kg

**fcst_ISO**

**d**

**c**

**fcst_UB**

## 2040 UTC

- Without GPS

- With GPS

- Truth

## 2200 UTC

- Without GPS

- With GPS

- Truth

## @0100, 13 June, 2002

**Initiation of new cells by colliding boundaries 5-7 hours into the forecast**

- radar observation            model forecast

## fcst_UB

- fcst_noSWV

- S-Pol obs

## @ 2300, 12 June, 2002

- S-Pol obs

- fcst_UB

- fcst_noSWV

## @ 0000, 13 June, 2002

- S-Pol obs

- fcst_UB

- fcst_noSWV

## @ 0030, 13 June, 2002

- S-Pol obs

- fcst_UB

- fcst_noSWV

## @ 0100, 13 June, 2002

- S-Pol obs

- fcst_UB

- fcst_noSWV

## Preliminary Conclusions

- The timing of the CI in the region without any significant low-level mesoscale forcing are very sensitive to the fine-scale structures in the moisture IC.
- GPS SWV data combined with surface moisture data can be effectively analyzed using our 3DVAR system to retrieve 3D moisture distribution, thus improves the prediction of the CI and subsequent evolution of convection.
- The anisotropic background error covariance helps better characterize detailed moisture structures dramatically. Thus the predicted reflectivity threat scores are higher.
- No significant positive impact on the exact timing and location of CI is found, due to the still insufficient data resolution to resolve structures at the 10 km scale.

## Thank you 

- Comments/Questions

## Sensitivity Studies

-  Effect of vertical filtering

-  With/without surface data

- Correlation coefficient w.r.t. vertical layer

## Analysis increments from a single sfc observation

- Obs=14.72 g kg-1
- Bg = 0 g kg-1
- Ana=14.69 g kg-1

*L**r** = 4 grid intervals*
*L**f **= 2 g/kg*

- Isotropic B

- Anisotropic B

- single sfc ob.

- Dryline

## List of Analysis Experiments

| Experiment | anisotropic Filter | RMSE (g kg-1) | CC | CC with EF |
| --- | --- | --- | --- | --- |
| ISO_RF | No | 0.35 | 0.84 | 0.83* |
| ANISO_RF | Yes | 0.28 | 0.91 | 0.93 |
| UB_RF | Yes | 0.34 | 0.86 | 0.83 |

****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 with RF

- ISO_RF: Isotropic **B **with RF

- analysis increment          truth-background

- CC=0.84; RMSE=0.35 g kg-1

- Recursive Filter

## ANISO_RF:

- Anisotropic **B **based on truth

- UB_RF:
- (covariance-updated)
- Anisotropic **B **based on the analysis with isotropic **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

## truth

- guess

- ISO

- UB

- ANISO

## Moisture analysis

- truth

- guess

- ANISO

- ISO

- UB