Michiko Masutani NOAA/NWS/NCEP/EMC RSIS/Wyle Information Systems Introduction to OSSE and Summary of...

Michiko Masutani

NOAA/NWS/NCEP/EMCRSIS/Wyle Information Systems

Introduction to OSSE and

Summary of NCEP OSSEs

http://www.emc.ncep.noaa.gov/research/OSSE

Nature Run: Serves as a true atmosphere for OSSEs

Observational data will be simulated from the Nature Run

Nature RunNature Run

DATAPRESENTATION

OSSE Quality Control

(Simulated conventional data)

OSSEDATA

PRESENTATION

Quality Control (Real conventional data)

OSSEDA

Real

TOVS

AIRS

etc.

DA

Simulated

TOVS

AIRS

etc.

NWP forecast

Existing data +

Proposed dataDWL, CrIS, ATMS, UAS, etc

Nature RunCurrent observing system

NWP forecastGFS OSSE

● OSSEs help in understanding and formulating observational errors

● DA (Data Assimilation) system will be prepared for the new data

● Enable data formatting and handling in advance of “live” instrument● OSSE results also showed that theoretical explanations will not be satisfactory when designing future observing systems.

Need for OSSEs♦Quantitatively–based decisions on the design and implementation of future observing systems

♦ Evaluate possible future instruments without the costs of developing, maintaining & using observing systems.

Benefit of OSSEs

Full OSSEThere are many types of simulation experiments. We have to call our OSSE a Full OSSE to avoid confusion.

● Nature run (proxy true atmosphere) is produced from a free forecast run using the highest resolution operational model.● Calibration to compare data impact between real and simulated data will be performed.● Data impact on forecast will be evaluated.● A Full OSSE can provide detailed evaluations of the configuration of observing systems.

Summary of results from NCEP OSSE

at NCEP

• Wintertime Nature run (1 month, Feb5-Mar.7,1993)• NR by ECMWF model T213 (~0.5 deg)• 1993 data distribution for calibration

• NCEP DA (SSI) withT62 ~ 2.5 deg, 300km and T170 ~1 deg, 110km

• Simulate and assimilate level1B radiance– Different method than using interpolated temperature as retrieval

• Use line-of- sight (LOS) wind for DWL– not u and v components

• Calibration performed• Effects of observational error tested• NR clouds are evaluated and adjusted

OSSE Calibration

• Compare real data sensitivity to sensitivity with simulated data

Impa

ct o

f w

ithd

raw

ing

RA

OB

win

ds

Results from OSSEs forDoppler Wind Lidar (DWL)

All levels (Best-DWL): Ultimate DWL that provides full tropospheric LOS soundings, clouds permitting.

DWL-Upper: An instrument that provides mid- and upper- tropospheric winds down only to the levels of significant cloud coverage.

DWL-PBL: An instrument that provides wind observations only from clouds and the PBL.

Non-Scan DWL : A non-scanning instrument that provides full tropospheric LOS soundings, clouds permitting, along a single line that parallels the ground track. (ADM-Aeolus like DWL)

Zonally and time averaged number of DWL measurements in a 2.5 degree grid box with 50km thickness for 6 hours. Numbers are divided by 1000. Note that 2.5 degree boxes are smaller in size at higher latitudes.

Estimate impact of real DWL from combination.

ADM-Aeolus with single payload Atmospheric LAser Doppler INstrument

ALADIN Observations of Line-of-Sight LOS wind profiles

in troposphere to lower stratosphere, up to 30 km with vertical resolution from 250 m - 2 km, horizontally averaged over 50 km every 200 km

Vertical sampling with 25 range gates can be varied up to 8 times during one orbit

High requirement for random error of HLOS <1 m/s (z=0-2 km, for Δz=0.5 km) <2 m/s (z=2-16 km, for Δz= 1 km), unknown bias < 0.4 m/s and linearity error < 0.7 % of actual wind speed; HLOS: projection on horizontal of LOS => LOS accuracy = 0.6*HLOS

Operating @ 355 nm with spectrometers for molecular Rayleigh and aerosol/cloud Mie backscatter

First wind lidar and first High Spectral Resolution Lidar HSRL in space to obtain aerosol/cloud optical properties (backscatter and extinction coefficients)

Atmospheric Dynamics Mission ADM-Aeolus

Doppler Wind Lidar (DWL) Impactwithout TOVS (using 1999 DAS)

Time averaged anomaly correlations between forecast and NR for meridional wind (V) fields at 200 hPa and 850 hPa. Experiments are done with 1999 DAS. CTL assimilates conventional data only.

3 3

8 8

No Lidar (Conventional + NOAA11 and NOAA12 TOVS)

Scan_DWL_Lower

Scan_DWL_upper

Non_scan_DWL

Scan_DWL

Synoptic S

caleT

otal scale

200 mb V 850 mb V

Forecast hour

Doppler Wind Lidar (DWL) ImpactWith TOVS (using 2004 DAS)

33

1.2 1.2

Dashed green line is for scan DWL with 20 times less data to make observation counts similar to non-scan DWL. This experiment is done with 2004 DAS.


Scan DWL_Lower

Scan DWL Uniformly thinned to 5%

Scan DWL_upper

Non-scan_DWL

Scan_DWL

Synoptic S

caleT

otal scale

200 mb V 850 mb V

2004 DAS CTL(Conventional + TOVS)

Data Impact of scan DWL1999 DAS vs. 2004 DAS

Scan DWL with 2004 DAS

1999 DAS CTL - CTL(99)

- CTL (99)

- CTL(99)

Differences in anomaly correlation

Synoptic ScaleTotal scale200 mb V


- 1999 DAS CTL

2004 DAS CTL(Conventional + TOVS)

Data Impact of scan DWL1999 DAS vs. 2004 DAS


1999 DAS CTL - CTL(99)

- CTL (99)

- CTL(99)


Synoptic ScaleTotal scale200 mb V

Impact of 2004 DAS

Impact of DWLImpact of DWL + better DAS


- 1999 DAS CTL

T62 CTL (reference)(Conventional data only)

Data Impact of scan DWL vs. T170

T62 CTL withScan DWL

T170 CTL


◊ CTL

X CTL+BestDWL

- CTL(T62)

- CTL(T62)

- CTL (T62)


Synoptic ScaleTotal scale

200 mb V

Synoptic Scale

T62 CTL (reference)(Conventional data only)

Data Impact of scan DWL vs. T170


T170 CTL


◊ CTL

X CTL+BestDWL

- CTL(T62)

- CTL(T62)

- CTL (T62)


Synoptic ScaleTotal scale

200 mb V

Impact of T170 modelImpact of DWL

Impact of DWL + T170

At smaller scales, adding DWL with scan is more important than increasing the model resolution

At planetary scale increasing the resolution is more important than adding DWL with scan

Effect of Observational Error on DWL Impact

• Percent improvement over Control Forecast (without DWL)

• Open circles: RAOBs simulated with systematic representation error

• Closed circles: RAOBs simulated with random errors

•Green: Best DWL Blue: Non-Scan DWL

3

8

3

8

Time averaged anomaly correlations between forecast and NR for meridional wind (V) fields at 200 hPa and 850 hPa. Experiments are done with 1999 DAS. CTL assimilates conventional data only.

DWL-Upper: An instrument that provides mid- and upper-tropospheric winds only to the levels of significant cloud coverage.

Operates only 10% (possibly up to 20%) of the time. Switched on

for 10 min at a time.

DWL-Lower: An instrument that provides wind observations from only clouds and the PBL.

Operates 100% of the time and keeps the instruments warm as well as measuring low level wind

DWL-NonScan: DWL covers all levels without scanning(ADM mission type DWL)

Targeted DWL experiments(Technologically possible scenarios)

Combination of two lidars

200mb

100% Upper Level

10% Uniform DWL Upper

10% Upper Level Adaptive sampling(based on the difference between first guess and NR, three minutes of segments are chosen – the other 81 min are discarded)

Doubled contour

(Feb13 - Mar 6 average )

Non-Scan DWL

CTLNo Lidar (Conventional + NOAA11 and NOAA12 TOVS)

100% Lower + 10% Uniform Upper

100% Lower + 100% Upper100% Lower + 10% Targeted Upper100% Lower + Non-Scan

Non-Scan only100% Lower

Anomaly correlation difference from controlSynoptic scale Meridional wind (V)

200hpa NH Feb13-Feb28DWL-Lower is better than DWL-Non-Scan only

With 100% DWL-Lower DWL-Non-Scan is better than uniform 10% DWL-Upper

Targeted 10% DWL-Upper performs somewhat better than DWL-Non-Scan in the analysis

DWL-Non-Scan performs somewhat better than Targeted 10% DWL-Upper in 36-48 hour forecast

OSSEs with Uniform Data

More data or a better model?

Fibonacci Grid used in the uniform data coverage OSSE

Data and model resolution

- Yucheng Song

Time averaged from Feb13-Feb2812-hour sampling200mb U and 200mb T are presented

Skill is presented as Anomaly Correlation %The differences from selected CTL are presented

40 levels of equally-spaced data 100km, 500km, 200km are tested

1000km RaobT170L42 analT62L28 fcst

CTL1000km RaobT62L28 anal & fcst

U 200 hPa

500km RaobT62L28 anal & fcst

500km Raob T62L64 anal T62L64 fcst


T 200 hPa

Benefit from increasing the number of levels

Increasing the vertical resolution was important for high density observations.

High density observation give better analysis but it could cause poor forecast

High density observations give a better analysis but could cause a poor forecast.

5

1000km RaobT170L42 analT62L28 fcst

CTL1000km RaobT62L28 anal & fcst

U 200 hPa

500km RaobT62L28 anal & fcst



T 200 hPa

Benefit from increasing the number of levels

Increasing the vertical resolution was important for high density observations.

High density observation give better analysis but it could cause poor forecast

T170 L42 model

L64 anl & fcst

High density observations give a better analysis but could cause a poor forecast.

L64 anl & fcst

L64 anl L28 fcst

L64 anl L28 fcst

5

500km obs

Results from OSSEs forDoppler Wind Lidar (DWL)

All levels (Best-DWL): Ultimate DWL that provides full tropospheric LOS soundings, clouds permitting.

DWL-Upper: An instrument that provides mid- and upper- tropospheric winds down only to the levels of significant cloud coverage.

DWL-PBL: An instrument that provides wind observations only from clouds and the PBL.

Non-Scan DWL : A non-scanning instrument that provides full tropospheric LOS soundings, clouds permitting, along a single line that parallels the ground track. (ADM-Aeolus like DWL)

Zonally and time averaged number of DWL measurements in a 2.5 degree grid box with 50km thickness for 6 hours. Numbers are divided by 1000. Note that 2.5 degree boxes are smaller in size at higher latitudes.

Estimate impact of real DWL from combination.

Forecast hour

Doppler Wind Lidar (DWL) Impact

33

1.2 1.2

Dashed green line is for scan DWL with 20 times less data to make observation counts similar to non-scan DWL. The results show definite advantage of scanning.This experiment is done with T62 model and SSI.


Scan DWL_Lower

Scan DWL Uniformly thinned to 5%

Scan DWL_upper

Non-scan_DWL

Scan_DWL

Synoptic S

caleT

otal scale

200 mb V 850 mb V

D2+D3: Red: Scan upper+scan Lower

D1: Light Blue closed circle: Best DWL (D1) with scan Rep Error 1m/s

R45: Cyen dotted line triangle:D1 with rep error 4.5m/s (4.5x4.5≈20)

U20: Orange: D1 uniformly thinned for factor 20 (Note this is technologically difficult)

N4: Violet:D1 Thinned for factor 20 but in for direction 45,135,225,315 (mimicking GWOS)

S10: Green dashed: Scan DWL 10min on 90 min off. No other DWL

D4 : Dark Blue dashed: non scan DWL

One or Four non-scan-DWL

NH V500Zonal wave number10-20

GWOS is better than ADM-Aeolus in theSH, Zonal wave 1-3

TW

GWOSLike lidar

ADM

UV

D2+D3: Red: Scan upper+scan LowerD1: Light Blue closed circle:scal-DWL repE1m/sR45: Cyen dotted line triangle: repE 4.5m/sU20: Orange: uniformly thinned to 5%N4: Violet:Four direction S10: Green dashed:Scan DWL 10min on 90 min off. D4 : Dark Blue dashed: non scan DWL

Scan-DWL has much more impact compared to non- scan-DWL with same amount of data.

If the data is thinned uniformly 20 times thinned data (U20) produce 50%-90% of impact.

20 times less weighted 100% data (R45) is generally slightly better than U20 (5% of data)

In fact U20 and R45 perform better than D1 time to time. This is more clear in 200hPa.

Simple GWOS type experiment (N4) showed significant improvement to D4 only in large scale over SH but not much better over NH and synoptic scale.

Without additional scan-DWL,10min on 90 off (S10)sampling is much worse than U20(5% uniform thinning) with twice as much as data.

Using T62 model forecast skill drop after 48 hr but this will improved with T170 particularly in wave number 10-20

The results are expected to change with GSI particularly with flow dependent back ground error covariance.

OSSE with one month long T213 nature run is limited. Need better nature run.

All results have to be confirmed by Joint OSSEs with Joint OSSE NR

Michiko Masutani NOAA/NWS/NCEP/EMC RSIS/Wyle Information Systems Introduction to OSSE and Summary of...

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