Forecast Sensitivity Observation Impact in East Asia and Arctic

Hyun Mee Kim1, Dae-Hui Kim1, and Sung-Min Kim21Yonsei University, 2Korea Meteorological Institute

1 December 20207th Workshop on the Impact of Various Observing Systems on NWP

Overview

Background

Forecast sensitivity observation impact (FSOI) in East Asia

Forecast sensitivity observation impact (FSOI) in Arctic

Implications

• Recently, the number of observations used in a data assimilation system is increasing enormously. Because it is not clear whether all these observations are always beneficial to the performance of the NWP, it is important to evaluate the impact of particular observations on the forecast quantitatively to provide relevant information about the impact of the observing system.

• Traditionally, the impact of observations has been assessed with observation system experiments (OSEs). The OSEs require much computational resources.

• The alternative way to evaluate the impact of observations on the forecast is the Forecast Sensitivity Observation Impact (FSOI).

• In this presentation, recent research results on FSOI in East Asia and Arctic are presented.

Background

Effect of enhanced satellite-derived atmospheric motion vectors on numerical weather prediction in East Asia

(Kim et al. 2017)

• The WRF model with 3DVAR and its adjoint are used to evaluate the impact of several types of observations, including enhanced satellite-derived atmospheric motion vectors (AMVs) that were made available during observation campaigns for two typhoons: Sinlaku and Jangmi, which both formed in the western North Pacific during September 2008.

• Model : WRFv3.3 and 3DVAR DA (27 km resolution)• Period : 25 August 2008 ~ 30 September 2008• Conventional observations (±3 hour window)

: SYNOP, SHIPS, BUOY, METAR, SOUND, PILOT, PROFILER, GPSPW, QSCAT, AMSUA• Enhanced AMVs (±1.5 hour window)

: from the MTSAT by CIMSS

Experimental setting and domain

ExperimentConventional observations Enhanced AMVs

Others AMV 1h AMV

EXP0 O X X

EXP1 O O X

EXP2 O O O

• The observation impact of the enhanced AMVs is large compared to the impact of conventional AMVs, but smaller than SOUND and AMSU-A.

• For all experiments, TB shows the largest observation impact followed by U and V.

• By assimilating the enhanced AMVs, observation impacts of U and V increase.

• When the enhanced AMVs are assimilated, the observation impact of AMSU-A decreases on average, especially for channel-7 which are mainly weighted in the upper troposphere.

FSOI

• Both the analysis and 24 h forecast distance error decrease significantly, when the enhanced AMVs are assimilated.

• During the period of the TCs, the 24 h forecast error for U and V is reduced by 4.9% when the enhanced AMVs are assimilated.

Typhoon forecast

• Without the assimilation of enhanced AMV data, radiosonde observations and satellite radiances show the highest total observation impact on forecasts.

• When enhanced AMVs are included in the assimilation, the observation impact of AMVs is increased and the impact of radiances is decreased.

• Enhanced AMVs improve forecast fields when tracking typhoon centers for Sinlaku and Jangmi. Both the model background and the analysis are improved by the continuous cycling of enhanced AMVs, with a greater reduction in forecast error along the background-trajectory than the analysis-trajectory.

Enhanced AMV effect

Effect of assimilating Himawari-8 atmospheric motion vectors on forecast errors over East Asia

(Kim and Kim 2018)

Experiment name geoAMV used for assimilationExp1 MTSAT-2 AMVs (QI ≥ 70)Exp2 HIMA-8 AMVs (QI ≥ 70)Exp3 HIMA-8 AMVs (QI ≥ 94)

Exp4MTSAT-2 AMVs (QI ≥ 70)+ HIMA-8 AMVs (QI ≥ 70)

Exp5MTSAT-2 AMVs (QI ≥ 70)+ HIMA-8 AMVs (QI ≥ 94)

• The energy-norm forecast error was reduced more by replacing MTSAT-2 AMVs with HIMA-8 AMVs than by adding HIMA-8 AMVs to the MTSAT-2 AMVs.

• When the HIMA-8 AMVs replaced or added to MTSAT-2 AMVs, the observation impact was reduced, which implies the analysis-forecast system was improved by assimilating HIMA-8 AMVs.

Forecast sensitivity observation impact in the 4DVAR and Hybrid-4DVAR data assimilation systems

(Kim and Kim 2019)

Date2014-8-5 2014-8-10 2014-8-15 2014-8-20 2014-8-25

Fore

cast

err

or re

duct

ion

[J k

g-1 ]

-4.8

-4.0

-3.2

-2.4

-1.6

-0.8

-0.4

0.0

0.4Hybrid-4DVAR: -12.42 J kg-1 day-1

4DVAR: -11.07 J kg-1 day-1

Total impact [J kg-1 day-1]

-1.6 -1.2 -0.8 -0.4 0.0 0.4

TCBOGUSCOMSCSRDropsonde

PRFLCOMSAMV

PILOTSHIPHIRS

GPSROMFG

MTSATMHS

ASCATMETAR

BUOYAIRSMSG

GOESSYNOP

AIRCRAFTTEMP

IASIAMSU-A

Hybrid-4DVAR4DVAR

Additional impact [J kg-1 day-1]

-0.20 -0.15 -0.10 -0.05 0.00 0.05

AMSU-AIASI

AIRSMHS

GPSROHIRS

COMSCSRGOES

MSGASCAT

MFGMTSAT

COMSAMVAIRCRAFT

TEMPPILOT

DropsondePRFL

SYNOPBUOY

METARSHIP

TCBOGUS

Satellite soundingSatellite windGround soundingSurface

Fraction of beneficial observation [%]30 35 40 45 50 55 60

TCBOGUSGPSRO

DropsondePRFLAIRS

AMSU-ACOMSAMVCOMSCSR

PILOTSYNOPMETAR

TEMPSHIP

AIRCRAFTIASI

MTSATHIRS

GOESASCAT

MHSMFGMSG

BUOY

Additional fraction [%]-2 0 2 4 10 12 14

HIRSMHS

COMSCSRAMSU-A

IASIAIRS

GPSROCOMSAMV

MTSATGOES

ASCATMFGMSG

DropsondeAIRCRAFT

PILOTTEMPPRFL

TCBOGUSBUOY

METARSYNOP

SHIP

a

c

b

d

• The observation impact was largest in AMSU-A followed by IASI, TEMP, AIRCRAFT, and SYNOP.

• The beneficial observation rate is approximately 50%.

• In Hybrid-4DVAR, the observation impacts for all observation types increase except for Dropsonde, PILOT, and wind profiler (PRFL), compared to those in 4DVAR.

• The increase of the beneficial observations in Hybrid-4DVAR is due to the smaller analysis error in Hybrid-4DVAR compared to the 4DVAR.

Forecast sensitivity observation impact in the 4DVAR and Hybrid-4DVAR data assimilation systems

(Kim and Kim 2019)

06 and 18 UTC analyses

Additional impact [J kg-1 day-1]

-0.15 -0.10 -0.05 0.00 0.05

AMSU-AIASI

AIRSMHS

GPSROHIRS

COMSCSRGOES

MSGASCAT

MFGMTSAT

COMSAMVAIRCRAFT

TEMPPILOT

DropsondePRFL

SYNOPBUOY

METARSHIP

TCBOGUS

Satellite soundingSatellite windGround soundingSurface

00 and 12 UTC analyses

Additional impact [J kg-1 day-1]

-0.15 -0.10 -0.05 0.00 0.05

a b

• The observation impact of AMVs in East Asia is sensitive to the integration time of the ensemble members used for deducing the flow-dependent BEC in Hybrid-4DVAR.

• Investigate the adjoint-based FSOI over the Arctic.

• Model : PWRF v3.8.1 and 3DVAR (30 km resolution)• Period : 26 July 2018 ~ 31 August 2018• Conventional observations (±3 hour window)

: SYNOP, SHIPS, BUOY, METAR, SOUND, PILOT, PROFILER, GPSPW, QSCAT, AMSUA

Forecast sensitivity observation impact in Arctic

FER and FSOI

Latitudinal distribution of FSOI

• Although generally similar, detailed FSOI results vary depending on the region, model, and DA system used.

• Beneficial observation rates increase in the regional modeling system compared to the global modeling system.

• The normalized observation impacts for SOUND and AMV near 90 N are considerably large compared to those in other latitudes, indicating the importance of the observing system in Arctic.

Implications

Thank you

슬라이드 번호 1Overview슬라이드 번호 3Effect of enhanced satellite-derived atmospheric motion vectors on numerical weather prediction in East Asia �(Kim et al. 2017) Experimental setting and domainFSOITyphoon forecastEnhanced AMV effectEffect of assimilating Himawari-8 atmospheric motion vectors on forecast errors over East Asia �(Kim and Kim 2018)Forecast sensitivity observation impact in the 4DVAR and Hybrid-4DVAR data assimilation systems �(Kim and Kim 2019)Forecast sensitivity observation impact in the 4DVAR and Hybrid-4DVAR data assimilation systems �(Kim and Kim 2019)Forecast sensitivity observation impact in ArcticFER and FSOILatitudinal distribution of FSOI슬라이드 번호 15슬라이드 번호 16