# Presentation: 079971

## Lidar Adaptive experiments

**http://****www.emc.noaa.gov/research/osse**

**OSSE  at NCEP**** **

**Lidar Adaptive experiments**

**Michiko Masutani, Jack Woollen, Yucheng Song, Dave Emmitt *, Zoltan Toth, Stephen Lord**

- EMC/NOAA/NWS/NCEP,  *SWA, JCSDA

**Toward Collaborative OSSEs**

## Michiko Masutani 1#, John S. Woollen 1 +, John LeMarshal4, Stephen J. Lord1, 

- Michiko Masutani 1#, John S. Woollen 1 +, John LeMarshal4, Stephen J. Lord1,
- Zoltan Toth1, Russ Treadon1, Yucheng Song 1+,
- Haibing Sun2 %, Thomas J. Kleespies2 ,
- G. David Emmitt3,  Sidney A. Wood 3 ,  Steven Greco 3,  Chris O’Handley3,
- Joseph Terry 5,  Weiyu Yang 1+, John C. Derber1, Paul VanDelst1 ,Jim Purser
- Many other people of EMC,  SWA, NESDIS, NASA/GSFC, JCSDA, and ECMWF

## OUTLINE

- Introduction for OSSE
- Adaptive Targeting Mission for NPOESS Hybrid DWL
- Results from DWL experiments
- Proposed sets of experiments and data counts
- Anomaly Correlation improvement
- Uniform data coverage experiments (preliminary)
- New Nature run proposal
- Summary
- Current NCEP OSSE system configuration

## Why OSSE?

- The cost of building satellite observing system is so high
- The cost of Observing system simulation experiments (OSSEs) is only a small fraction of the cost of the actual observing system
- OSSEs can help current operational data assimilation system to prepare handling new data and thus can accelerate operational use of the new data when available in the future
- OSSEs can provide valuable data impact guidance to optimize the usage of instrument

## Need for OSSE

- Quantitatively–based decisions on the design & implementation of future observing systems
- Evaluate possible  future instruments without cost of developing, maintaining & using observing systems.
*(The cost often exceeds $100 M / instrument)*
- Reducing the significant time lags between instrument deployment and eventual operational NWP us

- Conduct Simulated Observing Systems Experiments (OSSE)

- OSSE has been conducted to evaluate NPOESS  instruments

## Basic Concepts

**Preparation of the Nature Run**
- Truth for OSSEs
**Simulation of observed data**
- Must contains same kinds of errors as real observations (e.g., representativeness)
- Be produced by instrument models different from used those in  DA system (e.g., radiative transfer model)
**Calibration**
- Simulated observations should exhibit similar impact on system as real observations.
***Any difference should be explainable and consistent***
**Impact test**
- Analysis impact and forecast impact test

## Analysis and forecast are evaluate against analysis of control

- Simulated analysis and forecast are also evaluate against the Nature Run

## Winter time Nature run (1 month, Feb5-Mar.7,1993)
NR by ECMWF model T213  (~0.5 deg)
NCEP DA withT62 ~ 2.5 deg and  
	T170 ~1 deg
1993 data distribution for calibration.
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 component
Calibration performed
Effects of observational error tested
NR clouds are evaluated and adjusted

- NR by ECMWF model T213  (~0.5 deg)
- NCEP DA withT62 ~ 2.5 deg and
- 	T170 ~1 deg
- 1993 data distribution for calibration.
- 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 component
- Calibration performed
- Effects of observational error tested
- NR clouds are evaluated and adjusted

- Characteristics of NCEP OSSE

## OSSE Calibration

- Compare real data sensitivity to sensitivity with simulated data
**Relative order of impacts should be same for the same instruments**
**Magnitudes need not be the same but should be proportional**
**Quality control (rejection statistics)**
**Error characteristics (fits of background to Obs)**

- Data  impact depends on
- Data assimilation (DA) system
- Choice of the nature run.
- Observational error assignment.

## OSSE Calibration (Example)

- 500hPa U

## Adaptive Targeting Missionfor NPOESS Hybrid DWL

**Coherent detection sub-system (wedge scanner or HOE)**
**100% duty cycle **
**65W, 2.05microns **
**Lower tropospheric and enhanced aerosol/cloud winds**
**CMV height assignment**
**Reduce DAS observation error by ~2-3 m/s (per Chris Velden)**
**Depth of PBL**
**ICAT for direct detection + target identification by LEKF (e.g.)**
**Direct detection (molecular) sub-system (HOE)**
**10-15% duty cycle (aperiodic, i.e. adaptively targeted)**
**850W peak, 0.355 microns**
**Cloud free mid-upper tropospheric/ lower stratospheric winds**

**Simpson Weather Associates**

- 90/10 Rule

## Bracketing DWL experiments

## Experiments conducted

- Various combination of DWL-Lower and DWL-Upper
- D2D3: 100%L+100%U
- D2D3T3: 100%L+50%U (regular sampling)
- JW1: 100%L+10%U
- 		(Targeted  based on (obs-ges))
- T4: 100%L+10%U (NH Ocean)
- D2D3T2: 100%L+10%U (regular sampling)
-                     (to be redone)
- T6t: 100%L+10%U (Tropics)
- T6n: 100%L+10%U (NH band)
- D2: 100%L+0%U
- ALL experiments include conventional data and NOAA 11 NOAA12 TOVS radiance data.

## Anomaly correlation at NH  

- Anomaly correlation at NH
- Difference from D2 (100%LO)
- Synoptic scale Zonal wind (U)

- 200hPa

- 850hPa

- Adapteve

- 50%

- 100%

## Simulated direct detection subsystem data coverage

- (averaged from Feb 13 to Mar 6) – four cycles

## Doppler Wind Lidar experiments

-  The 90/10 rule implied a variety of choices, but which one is better?
-  How to design the targeting strategy so that the direct detection can be optimized?
-  The design of the direction detection has to put operation in mind
-  We have tested 8 experiments

## Data selection Cases

- (200mb Feb13 - Mar 6 average )

**100% Upper Level**

- (To be repeated  with more uniform distribution)

**10% Upper Level**
** **

**10% Upper Level tropics**

**50% Upper Level regular sampling**

- (To be repeated  with more uniform distribution)

## 10% Upper Level Adaptive sampling

**10% Upper Level NH band**

**10% Upper Level NH Ocean**

**10% Upper Level Adaptive sampling**
**(based on the difference of first guess and NR, three 3mins of segments are chosen – the other 81 mins discarded)**

## Experiments to be redone 

**100% Upper Level**

**10% Upper Level**

**50% Upper Level regular sampling**

- Doubled contour

**Experiments to be redone **

## Data coverage statistics on

**the  adaptive sampling case**

## Adaptive sampling based on error level

- The values are number of selected data within a 2.5 by 2.5 degree box

## Position of jet stream and adaptive sampling region

**The maximum sampling region located in the jet core**

## Hovmoller Diagrams of adaptive sampling and jet stream

- 25-55N average of data selection

- 25-55N average of zonal wind

- Jet region gets targeted more

## Diurnal cycle of the adaptive sampling region (Feb 13 ~ Mar 6 average)

- Diurnal cycle of the adaptive sampling region (Feb 13 ~ Mar 6 average)

- There has to be a match between satellite tracks and max error level regions

## Animation of adaptive data sampling 

- Animation of adaptive data sampling
-              ( full period every 6 hr)

## Animation of adaptive data sampling 

- Animation of adaptive data sampling
-                      (00Z cycle)

## Anomaly Correlation results (8 experiments)

- D2D3
- D2D3T3
- JW1
- T4
- D2D3T2
- T6t
- D2
- T6n

## Anomaly correlation at NH  

- Anomaly correlation at NH
- Difference from D2 (100%LO)
- Synoptic scale Zonal wind (U)

- 200hPa

- 850hPa

- Adapteve

- 50%

- 100%

## Anomaly correlation at NH 

- Anomaly correlation at NH
- Difference from D2
- Synoptic scale  Meridional wind (V)

- 200hPa

- 850hPa

## Anomaly correlation difference in NH 

- Anomaly correlation difference in NH
- from CTL (No DWL)
- Synoptic scale Zonal wind (U)

- 200hPa

- 850hPa

## Anomaly correlation at NH 

- Anomaly correlation at NH
- Difference from CTL (No DWL)
- Synoptic scale  Meridional wind (V)

- 200hPa

- 850hPa

## Anomaly correlation over tropics 

- Anomaly correlation over tropics
- Difference from D2
- Synoptic scale  wind 200mb

-       V

-        U

- NH Ocean

- NH Ocean

## 10% Upper DWL without targeting does not produce much impact (requires at least 50%)
A simple adaptive targeted DWL showed significantly better impact 
Target  regions correspond well with Northern Hemisphere jet stream
Adaptive DWL targeting is better than targeting NH Ocean (data sparse area) only
10% DWL direct detection improve low level wind forecast after 48 hrs

- A simple adaptive targeted DWL showed significantly better impact
- Target  regions correspond well with Northern Hemisphere jet stream
- Adaptive DWL targeting is better than targeting NH Ocean (data sparse area) only
- 10% DWL direct detection improve low level wind forecast after 48 hrs

- Summary for Targeted DWL experiments

## Highlight of the Results from DWL OSSEs

**Notes:**
Much of the impact of DWL_nonscan+TOVS come from TOVS for this diagram.   However The next figure which I did not show in the conference indicate the combined impact is much better the individual impact.

## Using 1999 version of NCEP DA  with  T62 model

- DWL with scan:
- 12 hour improvement
- DWL non scan:
- 4 hour improvement

- %

- NH synoptic scale

## Other finding through DWL OSSEs

- Upper level data become more important after 3 days even at lower levels
- Impact of DWL is more significant at smaller scales
- In tropics large analysis impacts diminish rapidly
- Need more model improvement to achieve forecast impact
- DWL will be useful in evaluating analysis
- Systematic large scale error added to the simulated data increase the data impact at large scale
- There are evidence that even non-scan lidar will produce an almost similar amount of impact as RAOB wind in NH average.
- RAOB wind has more impact over land. Non-scan lidar has more impact over ocean and tropics.
- Scanning is more important in upper troposphere than in lower troposphere

## Doppler Wind Lidar (DWL) Impactin Synoptic Scale

- Time averaged anomaly correlations between forecast and NR for meridional wind (V) fields at 200 hPa and 850 hPa.  Anomaly correlation are computed for zonal wave number from 10 to 20 components.  Differences from anomaly correlation for the control run (conventional data only) are plotted.

- Forecast hour

- Non Scan

- Scan Lidar

- Upper lidar become more important in low level

- 8

- 8

## 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 error
- Orange: Best DWL
-  Purple: Non- Scan DWL

**Total**

**Wave 10-20**

- Forecast length

- Significant impact in large scale by adding systematic large scale observational error

- DWL with large scale error

- DWL without large scale error

**Notes:**
If  (o-a) is added to the ctl data (CTL.adj) ,  impact of DWL with CTl.adl increase as difference between
CRL.adj and NR   is larger than difference between CTL and NR.

Dashed lines are difference  in Anomaly correlation  between NR and  CTL.adj +DWL, and NR  and CTL.adj only.

Solid lines are differences  in Anomaly correlation  between NR and  CTL  +DWL, and NR  and CTL only.

With (o-a) adjustment the impact are more clear in total fields.

## Impact of DWL with Scanning T170 vs. T62

**Notes:**
Characteristics of impact has not been altered significantly with an increase in model resolution.    
TOVS radiance is not included for this test.  Period used for this diagram  is from February 13 to 20.   12 hourly  data is used.

Data impact for the same scale will be reduced in the high resolution model.

Data impact is reduced with better models.
For the poorer model large data impact in analysis is rapidly reduced in forecast.

## Data Impact in T62 vs. T170

- T62 CTL with
- Scan DWL

- T170 CTL with
- Scan DWL

**◊  **CTL
- ● Non-ScanDWL
- X  Scan DWL

- T62

- T170

- Impact with T170 model look less than with T62 model

## Data Impact of scan DWL vs. T170

- T62 CTL with
- Scan DWL

- T170 CTL with
- Scan DWL

- Impact of T170 model

- Impact of DWL

- T170 is better than adding DWL with scan

- Adding DWL with scan is more important than T170 model

- Impact of DWL and T170

## Summary

- Need new nature run which will be used by many OSSEs.

- OSSE will be also used to designing ensemble system.

***The results also showed that theoretical explanations will not be satisfactory when designing future observing systems. ***

**The current NCEP system has shown that OSSEs can provide critical information for assessing observational data impacts.**** **

## Search for the New Nature Run

- Preparation of the nature run consume significant amount of resources.
- One or two good nature runs for many OSSEs
- The results can be compared.

- Plan for ECMWF Nature run Proposed by ECMWF and modified by NCEP and NASA
- -Two half year runs for summer and winter long run at T511L91 with detailed output every 3h-For selected periods generate high resolution nature  run with T799L91 with output every 30 minutes

**NCEP and NASA are planning to use nature run by ECMWF as the first nature run**

**Expecting sample data soon**

## New Nature Run (proposed) (ECMWF proposal modified by NCEP and NASA)

**Low resolution Nature Run (L-NR)**
- low resolution (T511, L91) with 3 hourly dump
- Two half year runs:    *Winter 0405, Summer 2005 are suggested*
- Extra month to remove the drift. (Discard the first month)
- Daily SST and  ICE (Provided by NCEP)

**High resolution NR**, T799 L91   30 min dump

**Archived  in MARS system**
**Dedicated machine for NR/THORPEX**

## Uniform coverage data OSSEs

- Equal area conventional data coverage
- Useful experiment test bed for current data assimilation system
- Can be used to study the differences among different observing systems – such as satellite vs. conventional
- More to come ...

## Fibonacci Grid used in the uniform data coverage OSSE 

**Fibonacci Grid used in the uniform data coverage OSSE**

## Nature’s way to prevent “over-seeding”

**Similarity between our data grid and a sunflower**

**Nature’s way to prevent “over-seeding”**