observing system simulation experiments at cimss

Observing System Simulation Experiments at CIMSS

By

CIMSS/OSSE Team :

Bob Aune ; Paul Menzel ; Jonathan Thom

Gail Bayler ; Chris Velden ; Tim Olander

and Allen Huang

Cooperative Institute for Meteorological Satellite Studies

University of Wisconsin

7 June, 1999

MESOSCALE OBSERVING SYSTEMSIMULATION EXPERIMENTS

(OSSE)GOAL

To assess the contribution of environmentalobserving systems to operational mesoscalenumerical weather forecasts in a controlledsoftware environment.

Future observing systems can be tested usingprojected instrument characteristics.

Current Research Plan

Initial Impact Study: Geostationary Interferometer

Simulated Products from “Nature” Atmosphere:

Soundings (T, Td)

Winds (Cloud drift / Water Vapor)

Soundings plus Winds

Soundings, Winds, Conventional Data

Radiances

Derived Products from Simulated Radiances

Retrievals (T, Td, derived products)

Winds (automated wind algorithm)

Direct radiance assimilation

PROCEDURES

Observations are synthesized from forecastsgenerated by a numerical prediction model thathas a known history calibrated against reality.

These forecasts represent truth and are referred toas the "nature" atmosphere.

Synthesized observations must mimic, as close aspossible, observations from the real observingsystem that is being evaluated.

Synthesized observations are assimilated into anassimilation system that is independent of the"nature" model.

Pilot Experiment

HYPOTHESIS:Information from a geostationary-basedinterferometer will significantly improve theaccuracy of numerical weather forecasts over thecurrent geostationary radiometer.

Temperature and moisture retrievals aresimulated by superimposing estimatedobservation errors on the "true" profilesgenerated by the "nature" run.

OSSE Design

An OSSE can be subdivided into four basic steps:

1) Generate a "nature" atmosphere

2) Compute synthetic observations

3) Assimilate the synthetic observations

4) Assess the impact on the resulting forecast

Each step is performed with the goal of minimizing anyexternal influences, which may compromise the value of thesynthesized observations, the assimilation process, or theresults of the numerical forecasts.

This OSSE is being conducted over a limited areadomain. The influence of pre-specified lateral boundariesmust be minimized.

1. Generate a "nature" atmosphere

University of Wisconsin, Nonhydrostatic Modeling System The horizontal domain is chosen to be as large as practical

to isolate the influence of the pre-defined lateral boundaryconditions. Horizontal resolution = 60 km.

Boundary conditions: NCEP Eta forecast model, NCEP 104grid.

Ideally, the "nature" atmosphere should be two to four timesthe resolution of the simulated observing system.

The model vertical resolution is chosen to be a minimum oftwo-times the resolution of the observing system to besimulated. Vertical levels = 38.

A 12hr forecast was generated to allow the model to "spinup".

UW-NMS Domain in the Eta 104 grid

“Nature” Calibration

2. Simulate observations

Temperature and moisture profiles from the "true"atmosphere are modified using realistic observationerrors.

Profiles of temperature and moisture are generated athourly intervals over the 12-hour analysis period.

A cloud mask is used to simulate gaps in the coverage.

NMS Grid Locations with Cloud Mask

RAOBS Surface

ACARS Profilers

Observation Errors

Ob type Count RMS Error BIASRAOB Temperature 98* 0.3 CRAOB Height 98* 8-32 mRAOB Dewpoint 98* 0.5 CRAOB Wind 98* .8 - 1.3 m/sSFC Temperature ~600 0.3 CSFC Dewpoint ~600 0.5 CSCF Wind ~600 0.4 m/sACARS Temperature ~3000 1.0 CACARS Wind ~3000 1.0m/sProfiler Wind 31* 1.0 m/sGEO-R Temperature ~3500* 1.9 - 2.1 C .27 CGEO-I Temperature ~4000* ~1.0 C 0.1 CGEO-R Mixing ratio ~3500* ~1.0 g/Kg .053 g/KgGEO-I Mixing ratio ~4000* ~0.5 g/Kg .02 g/Kg

(* indicates a profile)

Satellite Wind Errors

Ob type Level GEO-R GEO-IWinds, clear Count ~7000 ~10000

200mb na 3.5 m/s300mb 5.0 m/s 3.2 m/s400mb 4.5 m/s 3.0 m/s500mb 4.0 m/s 2.6 m/s700mb na 2.0 m/s

Winds, cloudy Count ~2000 ~4000200mb 4.5 m/s 3.0 m/s300mb 4.0 m/s 2.6 m/s400mb 3.5 m/s 2.3 m/s500mb 3.5 m/s 2.3 m/s700mb 3.0 m/s 2.0 m/s850mb 2.5 m/s 2.0 m/s

Simulated Error for TemperatureGEO-I GEO-R

0

100

200

300

400

500

600

700

800

900

1000

1100

0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5

Degrees C

Pre

ssu

re h

Pa

3. Assimilate the synthesized observations

The operational 40km Rapid Update Cycle (RUC) wasused to assimilate the observations at hourly intervals.

Boundary conditions: NCEP Eta model, projected ontothe AWIPS 211 grid (80km resolution).

Four assimilation experiments were performed:

1) Conventional observations only (CONV)

2) Geostationary radiometer (GEO-R); assimilate profilesadjusted to emulate a GOES-type system

3) Geostationary interferometer (GEO-I): assimilate profilesfrom a proposed geostationary interferometer

4) Perfect observation (BEST); assimilate "true" profilesextracted directly from the "nature" run

Note: The CONV and BEST experiments represent the range ofperformance that can be expected from the RUC system.

4. Assess the impact of the observations on theresulting forecasts

The impact of the observations will be assessed byobjectively measuring the ability of each observing system tosteer the resulting 12-hour forecasts toward the “true”atmosphere

Sensitivity of RUC analysis to retrieval density

GEO-R versus GEO-I

Retrieved T and Td

GEO-R versus GEO-I

Retrieved T and Td

GEO-R versus GEO-I

Retrieved T and Td

GEO-I results are significantly improvedover those from the GEO-R.

500 hPa temperature errors are reducedby 0.2 C root mean square (rms) over theextended CONUS (contiguous UnitedStates) and 700 hPa relative humidityerrors are reduced by 2%.

To compare the impact of the geostationaryinterferometer against the geostationary radiometer arelative score was computed using the No Observationrun (NO) and the Perfect Observation run (PO) tonormalize the verification statistics.

The RMS errors for temperature and relative humiditywere summed over four layers (700hPa, 500hPa, 400hPa,300hPa) and normalized between the RMS error sumsfrom the NO run and the PO run. A score of 10 matchesthe PO run.

Soundings + Winds 700hPa RH Validation

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

RMSE

(%)

CONV

GEO-R

GEO-I

BEST

Soundings + Winds 700hPa RH Validation

-6-5-4-3-2-1012

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

Bia

s (%

)

CONV

GEO-R

GEO-I

BEST

Soundings + Winds 700hPa RH Validation

30

35

40

45

50

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

S1 S

core

CONV

GEO-R

GEO-I

GEO-R versus GEO-I

Retrieved T and Td

Sat Winds

Conventional

Soundings + Winds 850hPa RH Validation

25

30

35

40

45

50

55

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

S1 S

core

CONV

GEO-R

GEO-I

Soundings + Winds 850hPa RH Validation

0

5

10

15

20

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

RM

SE

(%)

CONV

GEO-R

GEO-I

BEST

Soundings + Winds 850hPa RH Validation

-8

-6

-4

-2

0

2

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

Bia

s (%

)

CONV

GEO-R

GEO-I

BEST

GEO-R versus GEO-I

Retrieved T and Td

Sat Winds

Conventional

Soundings + Winds 500hPa T Validation

-0.4

-0.2

0

0.2

0.4

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

Bias

(deg

C0

CONV

GEO-R

GEO-I

BEST

Soundings + Winds 500hPa T Validation

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

RMSE

(deg

C) CONV

GEO-R

GEO-I

BEST

Soundings + Winds 500hPa T Validation

35

40

45

50

55

60

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

S1 S

core

CONV

GEO-R

GEO-I

GEO-R versus GEO-I

Retrieved T and Td

Sat Winds

Conventional

Soundings + Winds 300hPa T Validation

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

Bias

(deg

C)

CONV

GEO-R

GEO-I

BEST

Soundings + Winds 300hPa T Validation

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

RMSE

(deg

C) CONV

GEO-R

GEO-I

BEST

Soundings + Winds 300hPa T Validation

35

40

45

50

55

60

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

S1

Sco

reCONV

GEO-R

GEO-I

GEO-R versus GEO-I

Retrieved T and Td

Sat Winds

Conventional

Soundings + Winds 300hPa U

1

1.5

2

2.5

3

3.5

4

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

RMSE

(m/s

)

CONV

GEO-R

GEO-I

BEST

Soundings + Winds 300hPa U

30

35

40

45

50

55

60

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

S1 S

core

CONV

GEO-R

GEO-I

Soundings + Winds 300hPa U

-0.20

0.20.40.60.8

11.21.4

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

Bia

s (m

/s)

CONV

GEO-R

GEO-I

BEST

GEO-R versus GEO-I

Retrieved T and Td

Sat Winds

Conventional

Soundings + Winds 300hPa V

1

1.5

2

2.5

3

3.5

4

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

RM

SE

(m/s

) CONV

GEO-R

GEO-I

BEST

Soundings + Winds 300hPa V

-0.6-0.4-0.2

00.20.40.60.8

1

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

Bias

(m/s

)

CONV

GEO-R

GEO-I

BEST

Soundings + Winds 300hPa V

30

35

40

45

50

55

60

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour

S1

Sco

reCONV

GEO-R

GEO-I

GEO-R versus GEO-I

Retrieved T and Td

Sat Winds

Conventional

Plans for the Future

* Simulate winds from radiances

* Assimilate retrievals from radiances

* Assimilate radiances with 3DVar

* 14 day test periods (winter and spring)

* Resolve boundary condition issues

* Low Earth Orbit (LEO) OSSE

* Test other observing systems

Wind Experiment Using Simulated Radiances

1) Simulate radiances from GOES and from a geostationary interferometer using forward radiative transfer.

2) Put simulated radiances into the automated wind algorithm and generate cloud drift and water vapor winds

Hurricane Bonnie Wind and Cloud Fields

Wind Vectors :

Red - 1 km level

Green - 14 km

level

Clouds :

Light gray -

Ice Cloud

Dark Gray -

Water Cloud

GOES Radiances Simulation Verification

Wind Tracking Verification

Wind Tracking Verification - Continued

Tracking Interferometer Radiances

Tracking Mixing Ratio from Model