# Presentation: 430121

##  DAOS plans

- Presentation to the THORPEX Executive Committee
- 25 September 2008

## Merge with OS

- Work already covered through OPAG-IOS, rest of work would not constitute a WG. Best to combine, go to DAOS (Obs systems). J. Purdom (OPAG-IOS chair as rapporteur), C. Velden, R. Saunders, T. Keenan (radar), one in situ data. No work on innovative obs, should be done somewhere else.One co-chair from OS group (R. Saunders subject to approval of M-O and THORPEX) and F. Rabier resigns.
- Rewrite Strategy.

##  Data Assimilation and Observing Systems mission 

- The DAOS WG ensures  that THORPEX contributes to the optimisation of the use of the current WMO Global Observing System.
-   It contributes to the development of a well-founded strategy for the evolution of the GOS  to support NWP (primarily 1-14 days).

##  Data Assimilation and Observing Systems WG strategy

- It addresses issues in DA and improved understanding of the sources and growth of errors in analyses and forecasts
- It promotes research activities that lead to a better use of observations and  the understanding of their value
- It provides input and guidance for THORPEX regional campaigns for the deployment of observations to achieve their objectives
- This will be done in collaboration with the CBS OPAG-IOS

## Value of targeted data (1)

- Value of extra-tropical  targeted data has been found to be positive but small, on average
- Observations taken in sensitive areas have more value than observations deployed randomly
- Past experiments do not provide evidence of big impact obtained from just a few observations (when averaged over a large sample of cases)
- There are limitations due to the current assimilation methodologies (not yet fully flow-dependent)
- Sensitive areas characterization does not appear to be the first order problem
- Additional observations for tropical cyclones   have proven to be useful.

## Example of flow-dependent background-error covariances (here, from an ensemble Kalman filter)

-  EnKF make flow-dependent corrections to moisture field with non-moisture obs.
-  In USA, NCEP’s operational GSI (3D-Var) can only increment moisture with moisture (or moisture channel) obs.

- Single-observation
- example from data
- assimilation test using
- EnKF and NCEP GFS.
- EnKF ensemble mean
- background, PRMSL
- contoured in white.
- PWAT background
- in colors. Obs at
- yellow dot is 1 hPa
- smaller than
- background. Red
- contours = moisture
- analysis increment
- (solid +, dashed -)

## Value of targeted data (2)

- Recommendations
- Observation campaigns should be designed with science plans that take into consideration assimilation issues
- Expensive observation campaigns should not be justified based on current targeting strategies alone.
- Decision to undertake observation campaigns would benefit from preproject evaluation of expected value (e.g., using OSSEs or OSEs)

## Value of targeted data (3)

- Recommendations
- Additional benefit may be obtained from :
- Optimisation of existing operational resources
- Regional (vs highly localised) and systematic targeting during low predictability flow regimes on a continuous basis (periods of days to weeks)
- Adaptive processing and data selection of satellite data
- OSSEs would be useful to evaluate the impact of instruments and targeting strategies
- Collaborative effort on OSSEs based on nature run from ECMWF
- Calibration with respect to the impact of synthetic data
- Using OI adjoint based methods, synthetic data show a similar quantitative impact to real observations
- NASA will try this out to evaluate a space-based Doppler wind lidar instrument (similar to ADM)
- Evaluation of the anticipated impact of future instruments need to be made in the context of the future observing network and future modeling and assimilation systems

## OSSE calibration for Jan 2006 vs. Jan 2007 reference  *First Results*

**REAL OBS**

**OSSE**

- impact

- count

- J/kg

- J/kg

- Impact of **various observing systems** on GEOS-5 24h forecast error

- OSSE calibration for Jan 2006 vs. Jan 2007 reference  **First Results**

## Value of targeted data (4)

- Targeting for longer range forecasts
- NRL intends to investigate this topic
- Which flow regimes show lower predictability and what impact additional data may have.
- Cardinali et al. (2007) and Langland et al. (2008) have presented results on that. Can we make this distinction?
- Issues for targeting at shorter range remain and should be addressed before getting into longer range forecasts
- Evidence shows that removing or adding data does not lead to significant impact in the longer range
- Experiments from Kelly et al. (2007) show that removing data from the North Pacific does not have any impact on Europe at day-6

## Impact of denying observations over the Pacific on forecasts of increasing length in 3D-VAR

- Day 1

- Day 2

- Day 5

- Day 6

- Impact of denying observations over the Pacific on forecasts of increasing length in **3D-VAR**

- RMSE Denial – RMSE Control     500 hPa Height    00UTC Dec 2003-Feb 2004

- Kelly et al. 2007, *QJRMS*

***...******signal becomes global and diffused in the medium range***

## Impact of denying observations over the Pacific on forecasts of increasing length in 4D-VAR

- Day 1

- Day 2

- Day 5

- Day 6

- Impact of denying observations over the Pacific on forecasts of increasing length in **4D-VAR**

- RMSE Denial – RMSE Control     500 hPa Height    00UTC Dec 2003-Feb 2004

- Kelly et al. 2007, *QJRMS*

***...******medium range signal is even weaker in 4D-VAR (but less diffused)***

## Recommendations and Comments on Winter T-PARC 

- Predictability and Dynamical Processes
- THORPEX Working Group
-  &
- Data Assimilation/Observations
- THORPEX Working Group

## Recommendations / Comments (W T-PARC)

- 1) To facilitate the intercomparison of the results of the impact of targeted  observations,  it would be useful to have results from Winter Storm Reconnaissance 2001-2008 summarised as a review paper with the same metrics used in other studies.
- It would also provide sense of the change in impact as obs density has increased, data assimilation/modeling techniques have improved.
- 2) Hypotheses such as “the source of forecast errors could be a lack of amplitude in the forecasts of upper-tropospheric ridging in cyclone warm sector” could be tested  – can obs from W T-PARC be deployed to test this sort of hypothesis?
- 3) Potential to use additional obs to investigate bias corrections of satellite radiances and their use in cloudy regions.  Number from W T-PARC may be insufficient for this, but this is to be considered for future field campaigns.

## Recommendations / Suggestions in W T-PARC

- (4) Russian raob network in W T-PARC : do parallel tests of operational DA systems with/without this network to determine whether to sustain in future years.
- 		- This can also be explored/compared  with observation impact evaluation such as forecast sensitivity to observations.
- (5) Rapid-scan on MTSAT-2 from Japan unlikely to be available for W T-PARC, as it is only pre-operational now.   Explore GOES rapid-scan as part of W T-PARC planning
- (6) Explore the use of Australian BOM & CIMSS AMVs as a resource for W T-PARC

## Field experiments: T-PARC

- W T-PARC: extend the observation impact intercomparison.
- Understanding of differences/validation/evaluation with general study; look at sensitivity patterns + obs errors + B characteristics.
- Use additional obs during T-PARC and look at individual cases/data. Verification and metrics to be agreed on.
- W T-PARC: NRL, Env Canada, GMAO will evaluate the impact of observations
- S T-PARC: CAWCR and NRL have expressed an interest in performing re-analysis and evaluation of obs (OSEs and Sensitivity at NRL). M. Weissmann at ECMWF, GMAO and EC will test the impact of wind lidar.

## SV-sensitivity pattern for 48-h forecasts verifying over western North America during January 1998

- t=0 hrs

- t=48 hrs

*Gelaro et al. 2000, MWR*

## Field experiments: AMMA

- Quite a large impact of additional radiosonde data, and importance of calibrated humidity observations.
- Improvement in analysis fields and precipitation in the region of additional observations
- Observations have proven to be useful to diagnose model and assimilation performance and improve their quality (physical processes, background errors).
- Recommendation: These data are used operationally by NWP centres and it is very important to keep in situ data to help improve forecasting systems.
- A few centres will continue to look at impact of radiosonde data over Africa (forecast sensitivity to obs mainly). To support the African regional plan, evaluate the impact of AMDAR data.

## Precipitation FC

**Precipitation FC**
** ****(T+36-T+12)****‏**

**AMMA - preAMMA**

**preAMMA**

**GPCP**

**AMMA**

- Agusti-Panareda, 2008

**Notes:**
Symbols indicate the sonde type: eg. M: modem, 9: Vaisala RS92, 8: Vaisala RS80; B: both Vaisala RS80 and RS92.

##  IPY

- A few of the projects of the THORPEX-IPY cluster focus on improving the use of satellite data over the poles.
- Coordinate the work on satellite data assimilation over these regions
- Investigate the impact, in polar and other regions

## Cloud Detection (over Antarctica, similar studies over the Arctic)

**AIRS****: 10/01/2008**
** ****Cloud detection for  AIRS from Arpege**
**	****Comparison with reflectivity product from CLOUDSAT**

- IASI : 9/01/2008
- Example with channel 242 of IASI, with a max of  weight. function at 286hPa
- Comparison with MODIS product : cloud top Pressure (hPa)‏

- Goog detection over the peninsula

- cloud

- clear

- cloud

- clear

## Data assimilation plans

## Data assimilation at higher resolution 

- Global data assimilation is required to provide accurate boundary conditions
- Currently, downscaling from 4D-Var analysis yields better results than a mesoscale assimilation with a 3D-Var
- EnKF is also actively explored for this topic
- What is really necessary for mesoscale DA?

## RMS forecast errors v UK radiosondes winds 26/2/2006 to 15/3/2006.  

- RMS forecast errors v UK radiosondes winds 26/2/2006 to 15/3/2006.

- 850hPa

- 250hPa

- 3D-Var

- 3D-Var

- 4D-Var

- 4D-Var

- 850 hPa

- 200 hPa

## Potential benefits of mesoscale DA/NWP

- Higher resolution gives better picture of high-impact weather
- Higher resolution allows better assimilation and forecast of observed detail
- Can afford timely forecast using more recent observations.
- Limited area allows use of a tailored DA & NWP system.
- But there are caveats: boundary conditions, lack of connection with larger scales...

## Model error and weak-constraint 4D-Var

- Model error may be able to be assessed with ensembles. Exchange ideas and results on the subject with current EPS.
- TIGGE may be a resource for future studies (may need to be extended like for TIGGE-LAM)
-  Initial results in weak-constraint 4D-Var indicate that the **bias** component of model error is critical.

## Perturbation Growth versus Model Error

- Global ensemble forecast using stochastic physics

- New global analysis

**T+0**

**T+6**

**T+12**

**Forecast**
** ****Error**

**Perturbation Growth + Tuning =**** **

**Perturbation Growth + Model Error =**

## Temperature Error Growth at 500hPa (Model Error vs Pert. Growth)

**Model Error**
**Pert. Growth**

## Weak-constraint vs strong constraint 4D-Var experiments:

- Model error forcing formulation: 60N-90N model error forcing (K/h)

- ERA interim strong constraint 4D-Var
- W.C. 4D-Var with model error forcing formulation, Q confined to stratosphere and alphaq=0.3

**1 year parallel run started from June 1993:**
** **

**Weak-constraint vs strong constraint 4D-Var experiments:**
** **

- Lindskog et al, 2008

**Notes:**
The structure of this presentation will be as follows:
I will start with a brief introduction.
Some time is assigned to:
Defining extremes and changes of extremes
as well as on 
Modeling of extremes.
Focus will however be on a review of reults from modeling changes of extremes .
Something will be said about Cosequences and last
 A Summary

## aninc ERA interim

**Model level 5 (~1.5hPa)**
**Monthly mean temperature  **
**analysis increments for July 1993**

**aninc ERA interim**
** **

**aninc with model error forcing**
** **

**Notes:**
The structure of this presentation will be as follows:
I will start with a brief introduction.
Some time is assigned to:
Defining extremes and changes of extremes
as well as on 
Modeling of extremes.
Focus will however be on a review of reults from modeling changes of extremes .
Something will be said about Cosequences and last
 A Summary

## DAOS WG Tasks

- Provide guidance to the design of field experiments
- Observation campaigns should be designed with science plans that take into consideration assimilation issues
- Decision to undertake observation campaigns would benefit from preproject evaluation of expected value (e.g., using OSSEs or OSEs)
- Regional (vs highly localised) and systematic targeting during low predictability flow regimes on a continuous basis (periods of days to weeks)
- Support the use of well-calibrated OSSEs to evaluate the impact of new instruments
- Increase/optimise the usage of existing operational in-situ observations
- Explore adaptive processing and data selection of satellite data

## DAOS WG Tasks

- Review  the results from Winter Storm Reconnaissance 2001-2008 review paper as a guide for future planning
- Support testing of the impact of enhanced Russian raob network on operational DA systems to determine whether to sustain in future years.
- W T-PARC: extend the observation impact intercomparison.
- W T-PARC: NRL, Env Canada, GMAO will evaluate the impact of observations
- S T-PARC: CAWCR and NRL have expressed an interest in performing re-analysis and evaluation of obs (OSEs and Sensitivity at NRL). M. Weissmann at ECMWF, GMAO and EC will test the impact of wind lidar.

## DAOS WG Tasks

- In support of the African regional plan, encourage investigation of the impact of radiosonde and AMDAR data over Africa.
- Coordinate the work on satellite data assimilation over the polar  regions and investigate the impact, in polar and other regions

## DAOS WG Tasks

- Exchange ideas and results on the assessment of model error using ensembles.
- Investigate use of flow-dependent structure functions
- Evaluate the downscale impact of global scale improvements on the mesoscale
- Investigate issues on coupled DA and new data sets