Quantitative Design: The Right Way to Develop the Composite Observing System

A presentation to the GOES R Conference

Alexander E. MacDonald

NOAA Forecast Systems Lab – Boulder

May 11, 2004

Quantitative Design: The Right Way to Develop the Composite Observing System

Talk Summary

1. Observing subsystems MUST BE TREATED AS PART OF A COMPOSITE SYSTEM – not as stand alone systems.

2. We are now capable of credible simulations:

* Continental Scale

* Global Scale

3. Quantitative design is the right way to develop the composite observing system.

Quantitative Design: The Right Way to Develop the Composite Observing System

Talk Summary

1. Observing subsystems MUST BE TREATED AS PART OF A COMPOSITE SYSTEM – not as stand alone systems.

2. We are now capable of credible simulations:

* Continental Scale

* Global Scale

3. Quantitative design is the right way to develop the composite observing system.

Integrated

Global

Observing

The strategic triad of global observing:

Satellites – UAVs – Surface

Global Hawk could be the Unmanned Aerial Vehicle platform:

* Range: 14,000 miles

* Speed: 350 knots

* Altitude: 60,000 feet

* Payload: 1960 lbs

* Lease Cost: $ 4 M /plane

* Year ops: $ 3 M per plane

* ConOP: 2 aircraft, 25% duty cycle

* Prime: Northrop

AEM in situ

System Description:

The main idea of the Global Unified Profiling System is to take the most accurate possible profiles from the stratosphere to deep in the ocean over as much of the earth as possible. (Land too!)

The profiles should include state (T,p,u,v,q in atmosphere, temperature, current and salinity in the ocean), forcing, and chemistry.

Quantitative Design: The Right Way to Develop the Composite Observing System

Talk Summary

1. Observing subsystems MUST BE TREATED AS PART OF A COMPOSITE SYSTEM – not as stand alone systems.

2. We are now capable of credible simulations:

* Continental Scale

* Global Scale

3. Quantitative design is the right way to develop the composite observing system.

Results From the FSL Regional Lidar

OSSE•NOAA/FSL

- Steve Weygandt- Stan Benjamin- Steve Koch- Tom Schlatter- Adrian Marroquin- John Smart- Dezso Devenyi

• NOAA/NWS/NCEP - Michiko Masutani

• NOAA/ETL - Mike Hardesty- Barry Rye- Aniceto Belmonte- Graham Feingold

• NCAR- Dale Barker- Qinghong Zhang

Relationship between Global and Regional OSSEs

Global Nature Run

(ECMWF)

Global AssimilationRun (GFS)

Regional Nature Run

(MM5)

Regional Assimilation Run (RUC)

Global

Regional

Nature Run

AssimilationRun

Simulated Observations

Boundary Conditions

Boundary Conditions

Simulated Observations

Lidar Data Coverage

• Three satellite swaths per 12 h

• Profiles of two VLOS components at each point

0300UTC

0130UTC

0000UTC

0430UTC

OBSERVATION DATA COUNTS

Ob type Variables 12z 15zRaob (Z,T,Q,U,V) 3700 0Prof/VAD (U,V) 2600 2600ACARS (T,U,V) 1200 1300METAR/Buoy (T,Q,U,V) 1600 1600

Lidar (Vr) 1500 1500

Approximate no. of obs data points

• Lidar adds ~8% more wind obs at raob init times (00z, 12z)

• Lidar adds ~14% more wind obs at non-raob init times (06z, 18z)

Regional OSSE CalibrationDoes simulated-data impact (OSSE)

match real-data impact (OSE) for an existing observation type?

Real DataVerify against raobs

4-16 Feb 2001

15-20 Feb 1993

Simulated Data

Verify against nature run

Compare real-data and simulated-data ACARS denial

ACARS denial yields similar % degradation for real-data and OSSE simulated-data

Normalize Errors

NEGATIVE VALUE % degradation

POSITIVE VALUE % improvement

CNTLerror – EXPerror

CNTLerror

Impact of denying ACARS obson 6-h fcst vector wind RMSE

% degradation

• Lidar obs improve fcst more at non-raob init times• Lidar obs improvement greatest aloft

6- hourforecast

Non-raobinit time(06z,18z)

Raobinit time(00z,12z)

Assimilation of lidar observations

(but no lidar obs in boundary conditions)

Impact of adding lidar obson 6-h fcst vector wind RMSE

% improvement% degradation

The relative impact of the profiler data

0 5 10 15 20 25 30 35

100

200

300

400

500

600

700

800

900

"Relative" impact of ACARS and profiler datacompared to nodata exp on 3h RUC forecasts

profilerACARS

( denial - cntl ) / (nodata - cntl) X 100%

pre

ss

ure

(m

b)

profiler domain01035-01047 average

3-h Model forecast improvement

% improvement due to profiler and ACARS data

Profiler/ACARS impact calibrated by difference between 13-day experiments with all data and no observations (lateral boundary conditions only)

Fact: Profilers are the best data source for the lower part of the atmosphere within the network.

Satellite image taken at 0045 UT, during tornado outbreak.

When the profiler data is included, it doubles the “storm energy” that was predicted for the May 3, 1999 Oklahoma tornadoes.

Observing System Simulation must be an important part of our efforts to add new observing capabilities on the geostationary satellites.

The Potential Impact of Space-based Lidar Winds on Weather Prediction: Update on recent experiments at

the NASA DAO

Robert Atlas

Data Assimilation OfficeNASA Goddard Space Flight Center

Quantitative Design: The Right Way to Develop the Composite Observing System

Talk Summary

1. Observing subsystems MUST BE TREATED AS PART OF A COMPOSITE SYSTEM – not as stand alone systems.

2. We are now capable of credible simulations:

* Continental Scale

* Global Scale

3. Quantitative design is the right way to develop the composite observing system.

Important Community Efforts Should Embrace Quantitative Design of Observing Systems:

• Joint Center for Satellite Data Assimilation

• NCEP

• OAR Boulder Labs

• SSEC

• NAVY

• University community

• International community

• etc

Alexander.E.MacDonald@noaa.gov sandymacd@comcast.net