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COMET Satellite Meteorology Course April 3-13, 2000 Satellite Applications for Numerical Weather Prediction Bob Aune NOAA/NESDIS/ORA/ARAD/ASPT Cooperative Institute for Meteorological Studies (CIMSS) Madison, Wisconsin. Eta Analysis/Forecast Sensitivity - PowerPoint PPT Presentation
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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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
Data Type
B. 24-HR RMS Temperature Forecast Impact (July 14-23, 1999)
300 hPa
500 hPa
700 hPa
850 hPa
1000 hPa
-0.10
0.10.20.30.4
m/s
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
Data Type
D. 24-HR RMS Rel. Humidity Forecast Impact (July 14-23, 1999)
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
Data Type
B. 24-HR RMS Temperature Forecast Impact (July 14-23, 1999)
300 hPa
500 hPa
700 hPa
850 hPa
1000 hPa
-0.10
0.10.20.30.4
m/s
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
Data Type
D. 24-HR RMS Rel. Humidity Forecast Impact (July 14-23, 1999)
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
Data Type
B. 24-HR RMS Temperature Forecast Impact (July 14-23, 1999)
300 hPa
500 hPa
700 hPa
850 hPa
1000 hPa
-0.10
0.10.20.30.4
m/s
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
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
RAOB1 ACAR1 RAOB2 ACAR2 GMSLO
Data Type
D. 24-HR RMS Rel. Humidity Forecast Impact (July 14-23, 1999)
300 hPa
500 hPa
700 hPa
850 hPa
1000 hPa
T
U
RH
Z-0.5
0
0.5
1
Met
ers
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
Data Type
B. 24-HR RMS Temperature Forecast Impact (July 14-23, 1999)
300 hPa
500 hPa
700 hPa
850 hPa
1000 hPa
-0.10
0.10.20.30.4
m/s
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
Data Type
D. 24-HR RMS Rel. Humidity Forecast Impact (July 14-23, 1999)
300 hPa
500 hPa
700 hPa
850 hPa
1000 hPa
no-satsat
-0.06
0
0.06
0.12
0.18K
SSM/I GOESM TOVCD GOESC GOESW
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
SSM/I GOESM TOVCD GOESC GOESW
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.
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)
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
PW Operational Weights 4.51 % Improvement
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
PW Operational Weights 0.68 % Improvement
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
PW Operational Weights 1.60 % Improvement
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
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)
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
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
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
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
Sco
re 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
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)
850hPa 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
RMSE
(%)
CONV
GEO-R
GEO-I
BEST
Soundings + Winds 850hPa RH Validation
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
Conv+Geo-I vs Conv+Geo-Prfct (best = no noise)
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
LEO VS. GEO 850hPa RH Validation
-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
LEO VS. GEO 850hPa RH Validation
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