comet satellite meteorology course april 3-13, 2000

COMETSatellite Meteorology Course

April 3-13, 2000

Satellite Applications for Numerical Weather

Prediction

Bob AuneNOAA/NESDIS/ORA/ARAD/ASPT

Cooperative Institute for Meteorological Studies (CIMSS)

Madison, Wisconsin

Eta Analysis/Forecast Sensitivity

SSM/I, GOES Sounder, TOVS, GOES winds

RAOB, ACARS

GOES Data in Mesoscale Models3-layer Precipitable Water

Cloud Initialization

Cloud-track/Water Vapor Winds

Future PlatformsAdvanced Baseline Imager

Advanced Baseline Sounder

Observing System Simulation Experiments (OSSE)

Impact of Five Satellite Data Types inthe Eta Data Assimilation System during Three Seasons

by

Tom H. Zapotocny 1

W. Paul Menzel 1,2

James P. Nelson III 1

andJames A. Jung 1

1 Cooperative Institute for Meteorological Satellite Studies2 National Environmental Satellite, Data, and Information Service

Measure of 00-hr sensitivity and 24-hr forecast impact of five satellite data types assimilated into the EDAS for multi-day time periods covering three seasons (616 simulations). Data types examined are:

Special Sensor Microwave/Imager marine total PW (SSM/I)

GOES sounder marine three layer PW (GOESM)

TOVS marine cloudy temperature soundings (TOVCD)

GOES marine high-density cloud drift winds (GOESC)

GOES marine cloud top water vapor winds (GOESW)

Sensitivity and forecast impact of rawinsonde and aircraft data is also evaluated.

The following time periods were studied: 13-23 December 1998, 10-20 April 1999, 13-23 July 1999.

NCEP 80 km parallel runs were used for background.

EDAS was run at 80 km horizontal resolution and 38 levels vertically.

The data type being denied was unavailable to 3DVAR for the entire 11-day time period.

where D is the denied run, C is the control run, and A is the validating analysis

A positive forecast impact means the simulation was better with the particular satellite data included.

N

)CD( ySensitivit hr00

2ii

N

1i

N

)AC(

N

)AD( pactIm Forecast hr24

2ii

N

1i

2ii

N

1i

Evaluation criteria

Errors assigned to observations in the EDAS at five pressure levels. The data type, description and units are shown at left. Rawinsonde and ACAR temperature (RAOB1, ACAR1) and wind (RAOB2, ACAR2) errors are also included.

ID Type 1000 850 700 500 300 (hPa)

RAOB1 Temp (K) 1.2 0.8 0.8 0.8 0.9ACAR1 Temp (K) 1.5 1.1 1.0 1.0 1.0RAOB1 Sp Hum (%) 5.0 7.0 10.0 20.0 20.0TOVCD (M) Temp (K) 7.6 7.1 6.6 6.6 7.0SSM/I (M) PW (mm) 8.0 8.0 8.0 8.0 8.0GOESM (M) PW (mm) 8.0 8.0 8.0 8.0 8.0RAOB2 Wind (m/s) 1.4 1.5 1.6 2.1 3.0ACAR2 Wind (m/s) 2.5 2.5 2.5 2.5 2.5GOESC (MC) Wind (m/s) 1.8 1.8 1.9 2.1 3.0GOESW (MC) Wind (m/s) 1.8 1.8 1.9 2.1 3.0

M - Marine only C - Cloud only

-0.30

0.30.60.91.21.5

Perc

ent

SSM/I GOESM TOVCD GOESC GOESW

Data Type

D. 24-HR RMS Rel. Humidity Forecast Impact (Dec 14-23, 1998)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.30

0.30.60.91.21.5

Perc

ent


Data Type

D. 24-HR RMS Rel. Humidity Forecast Impact (April 11-20, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.30

0.30.60.91.21.5

Perc

ent


Data Type

D. 24-HR RMS Rel. Humidity Forecast Impact (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

Dec

Apr

Jul

Sat data impact on RH forecast

in three seasons

Dec

Apr

Jul

Sat data impact on T forecast

in three seasons-0.06

0

0.06

0.12

0.18K


Data Type

B. 24-HR RMS Temperature Forecast Impact (Dec 14-23, 1998)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.06

0

0.06

0.12

0.18K


Data Type

B. 24-HR RMS Temperature Forecast Impact (April 11-20, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.06

0

0.06

0.12

0.18

K


Data Type

B. 24-HR RMS Temperature Forecast Impact (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

02468

10

Met

ers


Data Type

A. 00-HR RMS Geopotential Height Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

0

0.2

0.4

0.6

0.8

K


Data Type

B. 00-HR RMS Temperature Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

00.30.60.91.21.51.8

m/s


Data Type

C. 00-HR RMS u-Component Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

02468

10

Perc

ent


Data Type

D. 00-HR RMS Rel. Humidity Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.5

0

0.5

1

Met

ers


Data Type

A. 24-HR RMS Geopotential Height Forecast Impact (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.06

0

0.06

0.12

0.18

K


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.10

0.10.20.30.4

m/s


Data Type

C. 24-HR RMS u-Component Forecast Impact (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.30

0.30.60.91.21.5

Perc

ent


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

Z

T

U

RH

00 24sat

02468

10

Met

ers

RAOB1 ACAR1 RAOB2 ACAR2 GMSLO

Data Type

A. 00-HR RMS Geopotential Height Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

0

0.2

0.4

0.6

0.8

K


Data Type

B. 00-HR RMS Temperature Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

00.30.60.91.21.51.8

m/s


Data Type

C. 00-HR RMS u-Component Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

0369

121518

Perc

ent


Data Type

D. 00-HR RMS Rel. Humidity Sensitivity (July 14-23, 1999)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.5

0

0.5

1

Met

ers


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.06

0

0.06

0.12

0.18

K


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.10

0.10.20.30.4

m/s


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.30

0.30.60.91.21.5

Perc

ent


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

Z

T

U

RH

00 24no-sat

-0.5

0

0.5

1

Met

ers


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.06

0

0.06

0.12

0.18

K


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.10

0.10.20.30.4

m/s


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.30

0.30.60.91.21.5

Perc

ent


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

T

U

RH

Z-0.5

0

0.5

1

Met

ers


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.06

0

0.06

0.12

0.18

K


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.10

0.10.20.30.4

m/s


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.30

0.30.60.91.21.5

Perc

ent


Data Type


300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

no-satsat

-0.06

0

0.06

0.12

0.18K


Data Type

A. 24-HR RMS Temperature Forecast Impact (Summary)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

-0.30

0.30.60.91.21.5

Per

cen

t


Data Type

B. 24-HR RMS Rel. Humidity Forecast Impact (Summary)

300 hPa

500 hPa

700 hPa

850 hPa

1000 hPa

Summary of Sat Impact on T and RH forecast for all three seasons

Conclusions

Large impact at 00-hr is largely reduced at 24-hr for sat and non-sat data alike

Each data type influences fields they do not observe as much as ones they do (eg. U affects RH)

Overall modest positive forecast impact from all five sat data types during all three seasons; only 28 / 295 forecasts negative impact

Cloud motion winds have most positive forecast impact overallespecially during the winter season.

Precipitable water has largest positive forecast impact during the summer and transition seasons.

During the summer season sat data provides as much or slightly more positive impact at 24-hrs than non-sat data.



RAOB, ACARS







EDAS/Eta Parallel Runs

• Operational GOES retrievals used• 5x5 field-of-view• 3-layers of PW (over land and water)• 5 inserts (every 3 hours) • 80 km • 38 levels• “fully-cycled”• 2 weeks out of the 10 months listed

What’s the Equitable Threat Score (ETS)?

(Hits - E)

(Hits + Misses + False Alarms + E)

# Forecast points x # Observed points

# of Total points possible

Rogers et al, Sept. 1996, Weather and Forecasting

ETS =

E =

In comparing to US raingauges, overall, the inclusion of GOES PW improves Eta precipitation forecasts. (This improvement is on the order-of-magnitude as the yearly historical average (‘87-’97)).

GOES Sounder Precipitable Water vapor (PW)

Cumulative Equitable Threat Scores by forecast time. The months represented are: October, November, December-1998, January, April, May, June, July, August and September-1999. There are approximately 150,000 total points for each forecast time.

Forecast time (Analysis times) Improvement

00 - 24 h (12 UTC runs) 2.2%

12 - 36 h (00 UTC runs) 2.5%

24 - 48 h (12 UTC runs) 2.0%

In the winter, the GOES PW only slightly improves precipitation forecasts

GOES Sounder Precipitable Water (PW)Winter Cumulative Eq. Threat Scores

(Oct-98, Nov, Dec, Jan-99)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 1 3 6 13 19 25 38 51

Rainfall (mm)

Eq

. T

hre

at S

core

49910 23301 15042 9920 4947 3138 2042 974 544 Total Cases

PW Denied

PW Operational Weights 0.83 % Improvement

In the summer, the GOES PW makes a more substantial improvement to the precipitation forecasts

GOES Sounder Precipitable Water (PW)Summer Cumulative Eq. Threat Scores

(May-99, Jun, Jul, Aug-99)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 1 3 6 13 19 25 38 51

Rainfall (mm)

Eq

. T

hre

at S

core

60613 29950 19522 12550 5570 3054 1809 556 205 Total Cases

PW Denied


GOES Sounder Precipitable Water (PW)Cumulative Threat Bias

(Oct-98, Nov, Dec, Jan, Apr, May, Jun, Jul, Aug, Sep-99)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 10 20 30 40 50 60Rainfall (mm)

Th

reat

Bia

sPW Denied


Threat Bias

GOES Sounder Precipitable Water (PW)Winter Threat Bias

(Oct-98, Nov, Dec, Jan-99)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 10 20 30 40 50 60

Rainfall (mm)

Th

reat

Bia

s

PW Denied


GOES Sounder Precipitable Water (PW)Summer Threat Bias

(May-99, Jun, Jul, Aug-99)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 10 20 30 40 50 60

Rainfall (mm)

Th

reat

Bia

s

PW Denied

PW Operational Weights -0.37 % Improvement

GOES Sounder cloud information can be used to improve regional models.

- CRAS

- RUC-II

- Eta/EDAS

Only the sounders have multiple CO2 channels.

ABI has only one such channel.

GOES-8/10 Sounder Cloud Data in NWP: Research

• Cloud information used in CIMSS Regional Assimilation System (CRAS) over both land and ocean.

• MAPS-2 is using hourly cloud-top information in continuous real-time cloud analysis experiments.

• Experiments with the NCEP Eta system have begun.

NOWCASTING/FORECASTING APPLICATIONS

•Combining both images can locate deep convection and major weather systems

•Thin clouds imply regions of radiational cooling

600 hPa 300 hPa

50% 98%

3-hour forecast: No Sounder data Coverage: CTP and TPW

Observed GOES-9 Sounder Image3-hour forecast: With Sounder data

More realistic moisture forecasts with GOES sounder data.

3-hour forecast: No Sounder data Coverage: CTP and TPW

3-hour forecast: With Sounder data Observed GOES-9 Sounder Image

More realistic moisture forecasts with GOES sounder data.

GOES CLOUD/PW DATA & NWP MODELS (CRAS)

24 hr Forecast w/o Sat CTP & PW

24 hr Forecast w Sat CTP & PW

GOES-8 11m Image

•The NWP model is initialized with Sat. CTP & PW

•Prior to start of forecast, Sat. CTP is inserted at 3 hourly intervals

•With Sat. data positive impact is seen over the eastern Pacific and central part of US

GOES CLOUD/PW DATA & NWP MODELS (CRAS)

24 hour CRAS Forecast w Sat CTP & PWGOES-8/10 7m Image

•The NWP model is initialized with Sat. CTP & PW

•Prior to start of forecast, Sat. CTP is inserted at 3 hourly intervals

•General water vapor structure is preserved

GOES CLOUD PRODUCT & NWP MODELS (RUC)00 UTC 23 March 1999

GOES Sounder-Derivedplus

Model-DerivedCloud Top Pressure

Impact of the GOES Sounder-

Derived Cloud Product

(Gray to Black indicatecloud added; Yellow to

Red indicate cloud removed by GOES data)

•Upper level relative humidity is improved for forecasts with cloud data

CURRENT STATUS of GOES SOUNDER CLOUDS

and the EDAS/ETA

James Jung1

1Cooperative Institute for Meteorological Satellite Studies (CIMSS)

UW-Madison

GOES Sounder Cloud Experiments• Consistent treatment of the Saturation Specific

Humidity with respect to ice in 3dvar. (This is extremely important to limit the over-prediction of clouds.)

• Cloud initiation threshold changed from 75%/85% for land/ocean to 97% everywhere.

• Cloud Water Mass added and removed as required from the GOES Sounder cloud product.

• Bogus Specific Humidity Observations derived to keep clouds in/out where necessary.

Control: with sat spec humidity fix, 75/85% cloud threshold

Experiment: with sat spec humidity fix, 97% cloud threshold,add/remove cloud, and add/remove specific humidity as necessary

Analysis -- High Clouds

Sounder Clouds

ExperimentControl

Clear LowHigh

Mid

Analysis -- Low Clouds

Sounder Clouds

ExperimentControl

12-hour forecast -- High Clouds

Sounder Clouds

ExperimentControl

12-hour forecast -- Low Clouds

Sounder Clouds

ExperimentControl

Clear

Eta 1-hr cloud forecasts -- Control (with Saturation Specific Humidity fix)

99157 01 - 06 UTC

Sounder 1-hr clouds

99157 01 - 06 UTC



RAOB, ACARS







NNATIONAL ATIONAL PPOLAR-ORBITING OLAR-ORBITING OOPERATIONAL PERATIONAL EENVIRONMENTAL NVIRONMENTAL SSATELLITE ATELLITE SSYSTEMYSTEM

SatelliteSatellite TransitionTransition

MODIS AIRSCERES(2) AMSU-AAMSR HSB

VIIRS GPSOSCMIS CrISSES ATMSDCS OMPSCERES

MOLSSSMISSESS

MODIS MISRCERES(2) MOPITTASTER VIIRS

CrISATMSCERES (TBD)

AVHRR IASI SEMAMSU-A MHS GOMESARSAT DCS ASCATT

VIIRS GPSOSCMIS SARSATSES TSISDCS ALT

AVHRR HIRSAMSU-A MHSSBUV SEMSARSAT DCS

OLSSSMISSESS

DMSP

DMSP

ABI/ABS Information

Timothy J. Schmit, W. P. Menzel, Robert M. Aune

NOAA/NESDIS/ORA Advanced Satellite Products Team (ASPT)

Allen Huang, Gail Bayler, Mat Gunshor,Jonathan Thom

Cooperative Institute for Meteorological Satellite Studies (CIMSS)

UW-Madison

Madison, Wisconsin

Information content (independent information using global covariance) analysis of the current GOES sounder, ABI and the ABS. A larger number denotes more information.

The ‘extended’ ABI does not even come close to giving the information content of the current sounder, much less the

next generation sounders.

For any number of parameters, an extended ABI is no replacement for even the current sounder.

Smaller values denote more retrieval skill.

Analysis of NOAA global raob data (tropics and mid-lat summer)

VAS - pastGOES - currentG18 - 18 1/2cm-1 chsG50 - 50 1/2cm-1 chsGAS - ABS 2000+ 1/2cm-1 chs

RAOB - T to 150mb (Q to 300mb)

GOES Info Content for Moist Atmospheres

0

2

4

6

8

10

12

14

16

18

VAS GOES G-18 G-50 GAS RAOB

Instrument

Nu

mb

er o

f In

dep

end

ent

Pie

ces

of

Info

rmat

ion

Temperature

Water Vapor

Geo-Interferometer nears Raob-like depiction of atmosphere

Only the ABS gives the needed (in year 2008) temperature accuracy of less than 1.0 K.

Only the ABS gives the needed (in year 2008) moisture accuracy of less than 20%.

IMG demonstrates interferometer capability to detect low level inversions: example over Ontario with inversion (absorption line BTs warmer) and Texas without (abs line BTs colder)

Water Vapor Structure for Tracking

Relative Humidity, %

Alti

tude

, km

NAST-I September 14, 1998 CAMEX-3

00:32 00:36 00:41 UTC125 km

This field of Relative Humidity was derived from interferometric data.

GIFTS Simulation of Hurricane Bonnie: Winds from Water Vapor Retrieval Tracking

Higher spectral resolution means more levels of winds can be determined.

Preliminary Findings for the Geo-Interferometer Observing System Simulation Experiment (OSSE)

at CIMSS

CIMSS/OSSE Team :

Bob Aune ; Paul Menzel ; Jonathan Thom ; Gail Bayler ; Chris Velden ; Tim Olander ; and Allen Huang

Cooperative Institute for Meteorological Satellite StudiesUniversity of Wisconsin

September 1999

Approach

Study impact of Geo Interferometer vs Geo Radiometer vs Leo Interferometer

Simulate products from “Nature” atmosphere:

Soundings (T, Td)

Winds (cloud drift / water vapor)

Aircraft Reports

Profiler Network

Conventional Data (sfc obs, raobs)

Use RUC as test model; assimilate obs 12 hrs;

forecast 12 hrs; use various combinations of obs

OSSE domain; influences of boundaries must be mitigated

Verification domain

RUC domain

RAOBS Surface

ACARS Profilers

Data coverage from components of conventional observing system

GEO-R Sounding Locations60 km spacing in clear skies

Observation Errors

Ob type Count RMS Error BIAS00/12 UTC:RAOB Temperature 98* 0.3 CRAOB Height 98* 8-32 mRAOB Dewpoint 98* 0.5 CRAOB Wind 98* .8 - 1.3 m/sHourly:SFC 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

Using only Geo soundings T and Td

No Data vs Geo-R vs Geo-I vs Geo-Prfct (no noise profiles)

700 RH

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

RM

SE

(%) CONV

GEO-R

GEO-I

BEST


-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


30

35

40

45

50

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

Hour

S1

Sco

re CONV

GEO-R

GEO-I

Using Geo soundings and winds

Conv (sfc obs, raobs, profiler, acars) vs Conv+Geo-R vs

Conv+Geo-I vs Conv+Geo-Prfct (best = no noise)

700hPa RH


25

30

35

40

45

50

55

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

Hour

S1

Sco

re CONV

GEO-R

GEO-I


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


-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

Using Geo soundings and winds

Conv (sfc obs, raobs, profiler, acars) vs Conv+Geo-R vs


850hPa RH


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


0

5

10

15

20

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

Hour

RMSE

(%)

CONV

GEO-R

GEO-I

BEST

Significant Finding from Geo-Interferometer OSSE

Geo Interferometer penetrates deeper providelow level moisture information:

Geo Radiometer only offers information above BL

Hourly Geo-I soundings and winds vs 6 hourly Leo-I

soundings

Conv (sfc obs, raobs, profiler, acars) vs Conv+Leo-I vs


850hPa RH

LEO VS. GEO 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

LEO

GEO-I

BEST


-4

-3

-2

-1

0

1

2

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

Hour

Bia

s (%

)

CONV

LEO

GEO-I

BEST


30

35

40

45

50

55

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

Hour

S1

Sco

re CONV

LEO

GEO-I

Conclusions (to date)

* RUC sensitive to moisture info at 50 km

* Geo-I has 2x temp/moisture info content than Geo-R

* Geo-R helps with 700hPa RH, but not 850hPa RH

* Geo-I resolves boundary layer moisture; Geo-I halves 850hPa RH RMSE of conv obs (10% to 5%).

* Geo impact appears to be linear with noise

* Leo-I does not equal Geo-I moisture performance;

hourly observations critical for regional model

Plans for the Future

* Assimilate retrievals from radiances

* Assimilate radiances with 3DVar

(super channels vs full spectrum)

* 14 day test periods (winter and spring)

* 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

Simulated Winds and Clouds for Hurricane

Wind Vectors :

Red - 1 km level

Green - 14 km

level

Clouds :

Light gray -

Ice Cloud

Dark Gray -

Water Cloud

Tracking Interferometer Radiances

Tracking Moisture from Model

comet satellite meteorology course april 3-13, 2000

Documents

satellite data types

acarsgoes data

aircraft data

hr forecast impact

particular satellite

forecast impact of rawinsonde

seasonsconclusionslarge

rh forecast