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The Impact of the High Temporal Resolution GOES/GOES-R Moisture

Information on Severe Weather Systems in Regional NWP model

The precipitation forecast of local storms and other

sever weather is very sensitive to the initial conditions

(or the analysis atmospheric fields). Both the observed

moisture information and the background fields directly

affect the initial conditions through data assimilation,

and then further affect the precipitation forecast. With

high temporal and spatial resolution, GOES-R’s

humidity information can improve regional/storm scale

data assimilation. The different background fields with

different spatial resolution are compared using the

regional numerical weather prediction (NWP) model.

The impacts of layer precipitable water (LPW) from

GOES Sounder with different background fields on

simulations of the accumulated precipitation of 2012

CONUS derecho are assessed using WRF-ARW / GSI

systems. The capability of 3D- and 4D-Var system is

studied on assimilating total precipitable water (TPW)

for tornado rainfall.

Our study focused on the following questions:

• How to use the LPW data in the assimilation system

and NWP model?

• How do the background fields affect the LPW

assimilation?

• What is the impact of the LPW data in regional NWP

model, especially for the precipitation forecasts?

Motivation

Pei Wang1, Jun Li1, Wei Cheng1,Yong-Keun Lee1, Zhenglong Li1, Jinlong Li1, Zhiquan Liu2, Tim Schmit3, and Steve Ackerman1

Analysis NCEP GFS ECMWF NCEP NAM

Resolution 0.5 degree 0.1406 degree 12 km

Top level 10 hPa 1 hPa 50 hPa

1. Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, Wisconsin

2. National Center for Atmospheric Research Earth System Laboratory, Boulder, Colorado

3. Advanced Satellite Products Team, NOAA/NESDIS/Center for Satellite Applications and Research, Madison, Wisconsin

Data and Experiment Design

Table I: Contingency table used in verification statistics

for dichotomous (et. Yes/No) forecasts and observations.

Forecast Verification Scores

Summary and Future work

• The LPW from GOES Sounder data could be assimilated

in GSI V3.1 successfully. The LPW affects the

precipitation through changing the moisture profiles in

the analysis fields.

• The precipitation is sensitive to the background fields.

Through comparison of background from NCEP GFS,

ECMWF and NCEP NAM, it shows that NCEP GFS provides

best precipitation forecasts. Therefore, the background

fields for assimilation of LPW data are based on NCEP

GFS analysis fields.

• The three layers PW data (High PW, Mid PW and Low Pw)

are assimilated separately. The assimilation of PW data

affect the analysis fields and then further influence the

precipitation.

• The ETS scores of precipitation could reflect the

performance of precipitation forecasting compared

with observations. It is shown that the combination of

high PW and low PW together could provide best

precipitation forecasts.

• The comparison of GSI-3Dvar and GSI-DRP4Dvar shows

that the temperature and moisture increments are

larger with GSI-DRP4Dvar.

Data

• Conventional Data (GTS)

• Layer Precipitable Water (LPW) based on the GOES-R

Advanced Baseline Imager (ABI) LAP algorithm.

• Three layers of LPW: sigma level values (0.3-0.7, 0.7-

0.9, and 0.9-1)

• Background data from NCEP FNL (BG: GFS)

• Background data from ECMWF operational (BG:

ECMWF)

• Background data from NCEP NAM (BG: NAM)

Fig 2. The 6-h accumulated precipitation of observations (Obs), NCEP GFS analysis as

background, ECMWF analysis as background and NAM analysis as background from

2012-06-29 18z to 30 00z

Acknowledgement: This work is supported by GOES-R and JPSS

Contact: Pei Wang, pei.wang@ssec.wisc.edu

• GTS+LPW (BG: GFS)

• GTS+LPW (BG: ECMEF)

• GTS+LPW (BG: NAM)

Models

• Data Assimilation: the Community Gridpoint

Statistical Interpolation (GSI V3.1).

• A foreword operator for LPW and the related module

has been implemented in GSI V3.1 system.

• Regional Forecast Model: Advanced Research WRF

(ARW) V3.6.1

Experimental Design

Fig.1 The model

domain

Forecast

Observation

Yes No

Yes Hits (YY) False Alarms (YN) YY + YN

No Misses (NY)

Correct rejections (NN)

NY + NN

YY + NY YN + NN Total = YY + YN + NY + NN

• Equitable Threat Score (ETS)

ETS = (Hits – Hits random) / (Hits + Misses + False

Alarms – Hits random)

Hits random = (Hits + False Alarms) (Hits + Misses) /

Total

Range: -1/3 to 1; 0 indicates no skill, and 1 indicates

perfect score.

Part I: Results

6-hour forecast precipitation

2012-06-29 18z to 30 00z

GFS Obs

ECMWF NAM

ETS of precipitation with assimilation GTS, High PW, Mid

PW and Low PW

2012-06-30 00z to 30 06z

Fig 4. The temperature increment and moisture increment of assimilation of TPW data

using GSI-3Dvar and GSI-DP4DVar system

Assimilation of LPW data

GTS GTS+HPW+MPW+LPW

GTS+HPW GTS+HPW+LPW

Fig 3. The 6-h accumulated precipitation of assimilation of GTS,

GTS+HighPW+MidPW+LowPW, GTS+HighPW and GTS+HighPW+LowPW from 2012-

06-29 18z to 30 00z

2012-6-29 18z to 30 00z

ETS scores 0.1 mm 1 mm 5 mm 10 mm

GTS 0.5393 0.4978 0.4243 0.2330

GTS+LPW(H) 0.5639 0.5403 0.4447 0.2315

GTS+LPW(M) 0.4881 0.4137 0.3066 0.1770

GTS+LPW(L) 0.5446 0.5093 0.4312 0.2364

GTS+LPW(HM) 0.5578 0.5386 0.4486 0.2412

GTS+LPW(ML) 0.5335 0.4925 0.4274 0.2309

GTS+LPW(HL) 0.5800 0.5644 0.4510 0.2302

GTS+LPW(HML) 0.5434 0.4854 0.4171 0.2958

Part II: Results

Comparison of the precipitation forecasts between GSI-

3Dvar and GSI-DRP4DVar system

GSI-3Dvar GSI-DRP4DVar

Table II: The ETS scores for precipitation. Red: the ETS

score is higher than GTS only. The high PW and low PW

together give us the highest ETS scores.

• Spin-up: 2012-6-29 06z to 12z

• Assimilation: 2012-6-29 12z

• Forecast: 2012-6-29 12z to

2012-6-30 12z

• Horizontal: 4 km

• Vertical: 52 levles, from surface to 10 hpa