Transcript Document

GOES-R RISK REDUCTION (R3) ACTIVITIES
Paul Menzel
NESDIS Office of Research and Applications
April 2004
End to End GOES-R System Plan
* User Requirements
set forth in GOES Users Conferences (OSD, ORA)
* Instrument Requirements
drafted in PORD (ORA, OSD, GSFC)
Tradeoffs between Inst Design and Science Req
dialogue with vendor (OSD, ORA)
Instrument Cal/Val
T/V and postlaunch checkout (ORA)
* Ground System /Archive Design and Implementation (OSD)
* Algorithm and Product Development
ATBDs (ORA)
simulations (ORA)
demonstration during science data gathering (ORA, JCSDA)
s/w architecture studies (ORA, OSDPD)
* Operations
s/w implementation (OSDPD)
science stewardship (ORA, NCDC)
archive (NCDC)
data assimilation (EMC)
End to End GOES-R System Plan (covered in GOES R3 plan)
* User Requirements
set forth in GOES Users Conferences (OSD, ORA)
* Instrument Requirements
drafted in PORD (ORA, OSD, GSFC)
Tradeoffs between Inst Design and Science Req
dialogue with vendor (OSD, ORA)
Instrument Cal/Val
T/V and postlaunch checkout (ORA)
* Ground System /Archive Design and Implementation (OSD)
* Algorithm and Product Development
ATBDs (ORA)
simulations (ORA)
demonstration during science data gathering (ORA, JCSDA)
s/w architecture studies (ORA, OSDPD)
* Operations
s/w implementation (OSDPD)
science stewardship (ORA, NCDC)
archive (NCDC)
data assimilation (EMC)
R3 enables efficient adoption of GOES-R data &
products into NOAA Wx and Climate services
within 6 months of routine operations
validation of radiometric GOES-R performance
unique first time ever imagery
examples of improved derived products for
weather and coastal ocean nowcasting
case studies of NWP impact
within one year
operational utilization of GOES-R data and
early products
Using GOES-R to help fulfill NOAA’s Mission Goals
(Ecosystems, Weather/water, Climate, and Commerce)
Timothy J. Schmit, W. P. Menzel, NOAA/NESDIS/ORA (Office of Research and Applications)
James J. Gurka, NOAA/NESDIS/OSD (Office of Systems Development)
Jun Li, Mat Gunshor, CIMSS (Cooperative Institute for Meteorological Satellite Studies)
Nan D. Walker, Coastal Studies Institute, Louisiana State University
GOES-R data and products will support all of NOAA’s four mission goals!
Enhanced GOES Capabilities Support NOAA Strategic Goals
Weather and Water
* Improved disaster mitigation with hurricane trajectory forecasts benefiting from better definition of mass and
motion fields.
* Improved knowledge of moisture and thermal fields provide better data for agricultural forecasting and nowcasting.
* Better general weather announcements affecting public health from improved forecasting and monitoring of surface
temperatures in urban and metropolitan areas during heat stress (and sub-zero conditions).
Climate
* Hourly high spectral resolution infrared calibrated geo-located radiances facilitate radiance calibration,
calibration-monitoring, and satellite-to-satellite cross-calibration of the full operational satellite system; and provide
measurements that resolve climate-relevant (diurnal, seasonal, and long-term interannual) changes in atmosphere,
ocean, land and cryosphere.
Ecosystems and Coastal Water
* Huge increase in measurements beneficial to ecosystem management and coastal & ocean resource utilization.
* First time ever, characterization of diurnal ocean color as a function of tidal conditions and observation of
phytoplankton blooms (e.g. red tides) as they occur.
* Improved coastal environment monitoring of a) response of marine ecosystems to short-term physical events, such
as passage of storms and tidal mixing; b) biotic and abiotic material in transient surface features, such as river plumes
and tidal fronts; and c) location of hazardous materials, such as oil spills, ocean waste disposal, and noxious algal
blooms
Commerce
* Better information regarding conditions leading to fog, icing, head or tail winds, and development of severe weather
including microbursts en route makes air traffic more economical and safer. Better depiction of ocean currents, low
level winds and calm areas, major storms, and hurricanes (locations, intensities, and motions) benefits ocean
transportation. Information regarding major ice storms, fog, flooding and flash flooding, heavy snowfall, blowing
snow, and blowing sand already assists train and truck transportation.
* Power consumption in the United States can be regulated more effectively with real-time assessment of regional and
local insolation as well as temperatures.
Major points for R3 Plan
R3 embraces all multi- & hyper -spectral experiences for GOES-R
preparation
AVIRIS, SHIS, NASTI, SeaWIFS, Hyperion, MODIS, AIRS, MSG,
IASI, CrIS, GIFTS
Time continuous hyperspectral data offer new opportunities
balance of temporal, spatial, and spectral for ocean and atm observations
Instrument characterization pre-launch
vacuum test experience with CrIS and GIFTS important
Aircraft, leo, geo-GIFTS (?), & simulated data used for science prep
near polar MODIS & AIRS and ER-2 in crop duster flights important
data over a variety of coastal and weather situations will be collected
R3 plan covers preparations for radiances and derived products
design options for ground system and archive considered
(implementation resourced elsewhere)
R3 plan covers FY04 through FY12
resources are distributed over 10 tasks
FY06 starts full strength preparations
R3 Tasks
Data processing and Archive Design (Task 0)
helps with timely design and continues advisory capacity during implementation
Algorithm Development (Task 1)
starts with ATBDs for GIFTS CDR, learns from aircraft and leo data,
& grows into prototype ops system
Preparations for Data Assimilation (Task 2)
starts early and expand just before launch
HES Design Synergy (Task 3)
continues to guide trade space between algorithms & instrument
Calibration / Validation (Task 4)
exploits CrIS and GIFTS TV in prep for GOES-R TV, prepares for field campaigns
Data Assimilation (Task 5)
big challenge is addressed early
Computer System for NWP (Task 6)
one time purchase plus annual maintenance
Data impact tests (Task 7)
many OSEs of different components of observing system
Nowcasting applications development (Task 8)
new products and visualizations
Education and Outreach and Training (Task 9)
distance learning tools & K-16 involvement
R3 provides the necessary elements for early
GOES-R utilization
(1)
(2)
(3)
(4)
(5)
capable informed users,
flexible inventive providers,
pre-existing data infrastructures,
informative interactions between providers and users,
knowledge brokers that recognize new connections
between capabilities and needs,
(6) champions of new opportunities in high positions,
(7) well planned transitions from research demonstrations to
operations, and
(8) cost effective use of GOES-R for improved coastal
ocean, weather & water, climate, and commerce
applications
R3 addresses challenges of GOES-R data utilization
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
better use over land,
better use in clouds,
better use in coastal regions
exploitation of spatial & temporal gradients measured by
satellite instruments
data compression techniques that don’t average out
3 sigma events (ie. retrievals versus super channels),
inter-satellite calibration consistency,
early demonstration projects before operations,
synergy with complementary observing systems
(ie. GPS and leo microwave),
sustained observations of oceans & atmosphere and
ultimately climate
R3 Partners
Activity
STAR CIMSS CIRA CICS CIOSS JCSDA OAR SSEC COMET
Grnd System
Cal/Val
Alg Dev
Images
Clouds
Soundings
X
Winds
New Products *
Assimilation X
Training
Outreach
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* refers to new product activities in surface, precipitation, ocean, radiation budget, and
ozone.
GOES-R improved products include
Imagery / Radiances
Sea Surface Temperature (SST)
Dust and Volcanic Ash Detection
Precipitation Estimations
Atmospheric Motions
Hurricane Location and Intensity
Biomass Burning / Smoke
Fog Detection
Aircraft Icing
Radiation Budget
Atmospheric Profiles
Water Vapor Processes
Cloud Properties
Surface Characteristics
Atmospheric Constituents
Ocean Color (Ocean water-leaving radiances or reflectances)
Chlorophyll concentration
Suspended sediment concentration
Water clarity / visibility
Coastal Currents
Harmful Algal Blooms
Coastal Normalized Difference Vegetation Index (NDVI)
Erosion and Bathymetric Changes
GOES-R HES temporal (15 min), spectral (0.5 cm-1),
spatial (1-10 km), & radiometric (0.1 K) capabilities will
* depict water vapor as never before by identifying small scale
features of moisture vertically and horizontally in the atmosphere
* track atmospheric motions much better by discriminating more
levels of motion and assigning heights more accurately
* characterize life cycle of clouds (cradle to grave) and distinguish
between ice and water cloud ( very useful for aircraft routing) and
identify cloud particle sizes (useful for radiative effects of clouds)
* measure surface temperatures (land and sea) by accounting for
emissivity effects (improved SSTs useful for sea level altimetry
applications)
* distinguish atmospheric constituents with improved certainty;
these include volcanic ash (useful for aircraft routing), ozone, and
possibly methane plus others trace gases.
Hyperspectral Reference for Intercalibration of Broadband Sensors
T = Geo - AIRS
Geo:
N
Tbb (K)
STD (K)
GOES-9
14
-0.63
1.04
GOES-10
16
-0.10
0.35
GOES-12
15
-0.13
0.55
MET-7
14
-0.87
0.38
MET-5
16
-1.93
.55
4 hrly LEO obs can’t monitor atm instability & cloud formation
Observations every 4 hours
are not often enough.
Atmospheric process not observed.
GOES obs monitor atmospheric instability and cloud formation
Hourly observations help track atmospheric changes
IR Spectral Coverage (DS or SW/M)
0.625 cm-1
Example 1
0.625cm-1
Example 2
0.625 cm-1
HES’
HES 1.25cm-1
0.6 cm-1
2.5cm-1
0.6 cm-1
O
z
o
n
e
C
O
2
“Traditional Side of
H2O absorption”
(T)
Important lines
for cloud
emissivity and
cloud type
CO N2O
Temperature
CO2 weak H2O
Atmospheric transmittance in
H2O sensitive region of spectrum

Studying spectral sensitivity
with AIRS Data

AIRS BT[1386.11] – BT[1386.66]
Spectral change of 0.5 cm-1
causes BT changes > 10 C
Twisted Ribbon formed by CO2 spectrum:
Tropopause inversion causes On-line & off-line patterns to cross
15 m CO2 Spectrum
Blue between-line Tb
warmer for tropospheric channels,
colder for stratospheric channels
Signature not available at low resolution
--tropopause--
SW
Characterizing Land and Sea
Surfaces
AIRS is enabling surface emissivity
estimates from atmospheric window
channel measurements. Example
shows sfc() over the
Mediterranean Sea to Algeria
to the Sahara Desert.
Transect from Mediterranean to Sahara
LW
LW
Wavenumber
SW
Inferring surface properties with AIRS high spectral resolution data
Barren region detection if T1086 < T981
T(981 cm-1)-T(1086 cm-1)
Barren vs Water/Vegetated
T(1086 cm-1)
AIRS data from 14 June 2002
Profile Retrievals in Cirrus Clouds with NAST-I
16.0 UTC
Depressions due to Cloud Attenuation
13.8
•
14.9
These retrievals, uncorrected for cloud
attenuation, demonstrate the ability to sense
spatial structure of moisture below a scattered
and semi-transparent cirrus cloud cover
AERI & Profiler Network
Depiction of Moisture Advection
evolution of
temperature and
moisture fields
using AERI data
evolution of wind
fields using
Profiler data
Best products will be realized from combinations of ABI
and HES (Hyperspectral Environmental Suite) data
(IR and Visible/near IR on the HES-Costal Water)!
Better cloud clearing, better spatial, etc
ABI
HES
Better surface emissivity, better spectral, etc
GOES-R spectral improvements provide more capability
to help see icing in clouds.
Example from MSG of Ch01 (0.6 um visible) and Ch03
(1.6 um NIR) seeing the development of deep convection
and the associated icing on 28 July 2003
pictures courtesy of EUMETSAT
CH03; 10.30
Ch01: black
Ch03: black
cloudfree
Ch01: white
Ch03: black
start of icing
(!?)
Ch01: black
Ch03: black
cloudfree
CH01; 10.30
Ch01: black
cloudfree
Ch01: white
cloud
Ch01: black
cloudfree
GOES-R spectral improvements help to see icing in clouds.
ABI Bands
Future
GOES
Imager
(ABI)
Band
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Wavelength
Range
(μm)
Central
Wavelength
(μm)
Sample Objective(s)
0.45-0.49
0.59-0.69
0.84-0.88
1.365-1.395
1.58-1.64
2.235 - 2.285
3.80-4.00
5.77-6.6
6.75-7.15
7.24-7.44
8.3-8.7
9.42-9.8
10.1-10.6
10.8-11.6
11.8-12.8
13.0-13.6
0.47
0.64
0.86
1.38
1.61
2.26
3.90
6.19
6.95
7.34
8.5
9.61
10.35
11.2
12.3
13.3
Daytime aerosol-over-land, Color imagery
Daytime clouds fog, insolation, winds
Daytime vegetation & aerosol-over-water, winds
Daytime cirrus cloud
Daytime cloud water, snow
Day land/cloud properties, particle size, vegetation
Sfc. & cloud/fog at night, fire
High-level atmospheric water vapor, winds, rainfall
Mid-level atmospheric water vapor, winds, rainfall
Lower-level water vapor, winds & SO2
Total water for stability, cloud phase, dust, SO2
Total ozone, turbulence, winds
Surface properties, low-level moisture & cloud
Total water for SST, clouds, rainfall
Total water & ash, SST
Air temp & cloud heights and amounts
Based on experience from:
Current GOES Imagers
MSG/AVHRR/
Sounder(s)
MODIS/MTG/
Aircraft, etc
GOES-R Coastal Water Imaging Function
• GOES-R provides first ocean color capability from geo orbit
– Can make measurements in constant tidal conditions
• GOES-R enables more frequent views of U.S. coastal ocean color
– Routine coverage of U.S. East Coast every 3 hours, with
1 hour refresh for high priority areas
• GOES-R provides more opportunities for cloud-free viewing
– Better detect/monitor/track rapidly changing phenomena such
as Harmful Algal Blooms, sediment plumes, and chaotic
coastal zone currents magnitude that could be underestimated
due to diurnal behavior
• GOES-R coastal water imaging function offers higher spatial
resolution (~300 meters)
– Fisheries researchers are limited by spatial resolution of
current systems—better than 1 km needed to improve
measurement and modeling of small scale phenomena such as
migration pathways for salmon fisheries
HES Coastal Waters Imaging Function
GOES-R enables
water type classification
Example from ER2 MAMS
Vis image (left), split IR window (right)
Fresh and salt water
identified in river delta ecosystems
GOES-R resolves more details
Current GOES Visible Image
Turbidity
Haze
Atoll Waters
MODIS examples from SSEC Direct Broadcast
GOES-R will help find answers to the following basic
science questions.
Can weather forecast duration and reliability be improved by new
remote sensing, data assimilation, and modeling?
How are global precipitation, evaporation, and the cycling of water
changing?
What are the effects of clouds and surface hydrologic processes on
weather and forecasting as well as climate?
Can satellite data contributions improve seasonal to inter-annual
forecasts?
Can satellite data contributions help to detect long-term change
(decadal to centennial time span)?
How are the oceanic ecosystems (open and coastal) changing? What
portions are natural versus anthropogenic?