# Presentation: 030710

## Global OSSEs at NCEP

**Stephen J. Lord**

**NOAA/NWS/NCEP/EMC**

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

**Global OSSEs at NCEP**

## Co-Authors and Contributors

- Haibing Sun2 %, Thomas J. Kleespies2, G. David Emmitt 3,  Sidney A. Wood3
- Bert Katz1 +, Russ Treadon1, John C. Derber1, Steven Greco3, Joseph Terry4

- 1NOAA/NWS/NCEP/EMC, Camp Springs, MD
- 2NOAA/NESDIS, Camp Springs, MD
- 3Simpson Weather Associates, Charlottesville, VA
- 4NASA/GSFC, Greenbelt, MD
- #RS Information Systems
-  +Science Applications International Corporation
- %QSS Group, Inc.

- *Corresponding Author Address:
- Michiko Masutani, 5200 Auth Road Rm 207
- Camp Springs, MD 20746
- E-mail:michiko.masutani@noaa.gov

**Co-Authors and Contributors**

## Overview

- Introduction to OSSEs
- Motivation
- Basic Concepts
- The NCEP OSSE System
- Components
- Testing
- Preliminary NCEP results for Doppler Wind Lidar (DWL)
- Planned upgrades
- Conclusions

## Introduction to OSSEs:  Motivation

- Costs of developing, maintaining & using observing systems often exceed $100 M/instrument
- To date, there have been no quantitatively–based decisions on the design & implementation of future observing systems
- Significant time lags between instrument deployment and eventual operational NWP use

## Introduction to OSSEs:  Motivation (cont)

- Doing OSSEs will
- Enable data formatting and handling in advance of “live” instrument
- Exercise preliminary quality control algorithms provided simulated obs have realistic properties
- OSSEs can provide quantitative information on observing system impacts and data assimilation system components
- Requirements definition and instrument design for new instruments
- Evaluate alternative mix of current instruments
- Data assimilation system diagnosis and improvement
- Understanding and formulation of observational errors
- There is no perfect solution but more information can lead to better planning and decisions

## Introduction to OSSEs Basic Concepts

**New & Existing Observations With & Without**

- /OSSE

**Nature Run**

- Data Assimilation System

- Real

**Existing Real Observations**
**With & Without**

**Nature**

**Truth**

## Introduction to OSSEs: Basic Concepts (cont)

- Simulated observations should
- Exhibit same system impact as real observations
- Contain same kinds of errors as real observations (e.g., representativeness)
- Nature Run is truncated spectrally in space & time
- Real Nature is not truncated
- Be produced by different instrument models than used in data assimilation system (e.g., radiances)

## Current NCEP OSSE System Components

- Nature Run (NR)
- ECMWF Reanalysis Model (ERA-15)
- Resolution T213/L31 (~60 km)
- 06 UTC 5 Feb 1993 – 00 UTC 7 March 1993
- Climatologically representative period
- Good agreement in synoptic behavior
- Clouds need adjustment
- Observations
- Conventional (raob, surface, pireps, MDCRS (1993))
- HIRS, MSU NOAA-11, NOAA-12
- Cloud track winds
- NCEP Global Data Assimilation System
- 1999 version
- T62/L28 (200 km horizontal resolution)
- Radiance assimilation

## Evaluation of NR clouds and adjustment

- Frequency distribution (in %) for ocean areas containing low level cloud cover in 20  5%- band categories.
- Solid line: NR cloud cover without adjustment.
- Dashed line:  with adjustment.

- NR clouds are evaluated and adjusted.

## Testing: 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)**

## 500 hPa Height Anomaly Correlation 72 hour forecasts

- 500 hPa Height Anomaly Correlation 72 hour forecasts

- Calibration of Simulated Data Impacts Vs Real

## Observational Error FormulationSurface & Upper Air

**Nature Run**

**NCEP Model**

**Real**

- Observational Error FormulationSurface & Upper Air

**Observation simulated at the Nature Run Surface**

**Observation simulated at the Real Surface**

****

****

## Observational Error FormulationSurface & Upper Air

-  With random error:
-   Data rejection rate too small (top)
-   Fit of obs too small (bottom)

- time

- Z

- Observational Error FormulationSurface & Upper Air

- Simulated with Systematic
- representation error

## Preliminary NCEP results for Doppler 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 only down to  the levels of significant cloud coverage.
**DWL-PBL**:  An instrument that provides only wind observations 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.

- Number of DWL LOS Winds  2/12/93

## Doppler Wind Lidar (DWL) Impact

- 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

## 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

- Forecast length

**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.

## Planned Upgrades

- Advanced instruments (e.g. AIRS) and impact experiments at higher resolution require larger computing resources, more realistic observations distribution and Nature Run and advanced data assimilation
- Computing
- Current: 32 processor SGI O2000
- Future: share former NCEP operational computer (IBMSP) until April 2004 (NPOESS funding)
- Data Assimilation
- Current: 1999 version of SSI and Global model
- Future: 2003 version of SSI and model
- Advanced radiative transfer for hyperspectral sounders
- Improved computational efficiency
- Prognostic cloud water
- Improved mountain drag
- Observations and Nature Run
- Current: 1993 obs distribution; 4 weeks, T213 NR
- Future: 2003-4 obs distribution; longer, higher resolution NR

## Conclusions

- OSSE methodology has matured into scientifically valid, but not perfect, approach for evaluating
- Instrument design
- Cost – capability tradeoffs
- Observing system design
- Impact of components
- Data assimilation system behavior
- NCEP system has been developed
- Attention to details of obs simulation, Nature Run calibration
- DWL design is a test case
- Can be applied to other NPOESS instruments (e.g. CrIS)
- Can be used for observing system design (e.g. THORPEX)
- Preliminary results for DWL give encouraging impact
- Future testing at higher resolution and improved data assimilation system
- More realistic observations (1993 -> 2004) and Nature Run