GOES-R RISK REDUCTION (R3) ACTIVITIES

Paul Menzel

NESDIS Office of Research and Applications 

May 2004

End to End GOES-R System Plan

* User Requirementsset forth in GOES Users Conferences (OSD, ORA)

* Instrument Requirementsdrafted in PORD (ORA, OSD, GSFC)

Tradeoffs between Inst Design and Science Reqdialogue with vendor (OSD, ORA)

Instrument Cal/ValT/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)

* Operationss/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 Requirementsset forth in GOES Users Conferences (OSD, ORA)

* Instrument Requirementsdrafted in PORD (ORA, OSD, GSFC)

Tradeoffs between Inst Design and Science Reqdialogue with vendor (OSD, ORA)

Instrument Cal/ValT/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)

* Operationss/w implementation (OSDPD)science stewardship (ORA, NCDC)archive (NCDC)data assimilation (EMC)

R3 provides the necessary elements for early GOES-R utilization

(1) capable informed users, (2) flexible inventive providers, (3) pre-existing data infrastructures, (4) informative interactions between providers and users, (5) 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 enables efficient adoption of GOES-R data & products into NOAA Wx and Climate services

within 6 months of routine operationsvalidation of radiometric GOES-R performanceunique first time ever imageryexamples of improved derived products for weather and coastal ocean nowcastingcase 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 GoalsWeather 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 bloomsCommerce* 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 prepnear polar MODIS & AIRS and ER-2 in crop duster flights importantdata over a variety of coastal and weather situations will be collected

R3 plan covers preparations for radiances and derived productsdesign options for ground system and archive considered(implementation resourced elsewhere)

R3 plan covers FY04 through FY12resources are distributed over 10 tasksFY06 starts full strength preparations

R3 TasksData processing and Archive Design (Task 0)

helps with timely design and continues advisory capacity during implementationAlgorithm 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 addresses challenges of GOES-R data utilization

(1) better use over land, (2) better use in clouds, (3) better use in coastal regions(4) exploitation of spatial & temporal gradients measured by

satellite instruments(5) data compression techniques that don’t average out 3

sigma events (ie. retrievals versus super channels), (6) inter-satellite calibration consistency,(7) early demonstration projects before operations,(8) synergy with complementary observing systems

(ie. GPS and leo microwave),(9) sustained observations of oceans & atmosphere and

ultimately climate

R3 Partners

Activity STAR CIMSS CIRA CICS CIOSS JCSDA OAR SSEC COMET Grnd System X Cal/Val X X X Alg Dev Images X X X Clouds X X X X Soundings X X X X X Winds X X X X

New Products * X X X X X X Assimilation X X X X X X Training X X X Outreach X X X X * refers to new product activities in surface, precipitation, ocean, radiation budget, and ozone.

GOES-R improved products includeImagery / Radiances

Sea Surface Temperature (SST)Dust and Volcanic Ash Detection

Precipitation EstimationsAtmospheric Motions

Hurricane Location and IntensityBiomass Burning / Smoke

Fog DetectionAircraft Icing

Radiation Budget Atmospheric Profiles

Water Vapor ProcessesCloud Properties

Surface CharacteristicsAtmospheric Constituents

Ocean Color (Ocean water-leaving radiances or reflectances)Chlorophyll concentration

Suspended sediment concentrationWater clarity / visibility

Coastal CurrentsHarmful Algal Blooms

Coastal Normalized Difference Vegetation Index (NDVI)Erosion and Bathymetric Changes

Three Functions

HES, ABI, and CWI

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.

0.625cm-1 1.25cm-1 2.5cm-1

0.625 cm-1 0.625 cm-1

0.6 cm-1 0.6 cm-1

CO2

(T)

Important lines for cloud emissivity and cloud type

Ozone “Traditional Side of

H2O absorption”

CO2 weak H2OCO N2O Temperature

Example 2

Example 1

IR Spectral Coverage (DS or SW/M)

5

HES

HES’

Atmospheric transmittance inH2O sensitive region of spectrum

Spectral change of 0.5 cm-1 causes BT changes > 10 C

AIRS BT[1386.11] – BT[1386.66]

Studying spectral sensitivitywith AIRS Data

Twisted Ribbon formed by CO2 spectrum: Tropopause inversion causes On-line & off-line patterns to cross

Blue between-line Tb warmer for tropospheric channels,colder for stratospheric channels

Signature not available at low resolution

15 m CO2 Spectrum

--tropopause--

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)!

ABI HES

Better cloud clearing, better spatial, etc

Better surface emissivity, better spectral, etc

Future GOES Imager (ABI) Band

Wavelength Range (μm)

Central Wavelength

(μm) Sample Objective(s)

1 0.45-0.49 0.47 Daytime aerosol-over-land, Color imagery 2 0.59-0.69 0.64 Daytime clouds fog, insolation, winds 3 0.84-0.88 0.86 Daytime vegetation & aerosol-over-water, winds 4 1.365-1.395 1.38 Daytime cirrus cloud 5 1.58-1.64 1.61 Daytime cloud water, snow 6 2.235 - 2.285 2.26 Day land/cloud properties, particle size, vegetation 7 3.80-4.00 3.90 Sfc. & cloud/fog at night, fire 8 5.77-6.6 6.19 High-level atmospheric water vapor, winds, rainfall 9 6.75-7.15 6.95 Mid-level atmospheric water vapor, winds, rainfall

10 7.24-7.44 7.34 Lower-level water vapor, winds & SO2 11 8.3-8.7 8.5 Total water for stability, cloud phase, dust, SO2 12 9.42-9.8 9.61 Total ozone, turbulence, winds 13 10.1-10.6 10.35 Surface properties, low-level moisture & cloud 14 10.8-11.6 11.2 Total water for SST, clouds, rainfall 15 11.8-12.8 12.3 Total water & ash, SST 16 13.0-13.6 13.3 Air temp & cloud heights and amounts

Current GOES ImagersMODIS/MTG/ Aircraft, etc

ABI Bands

MSG/AVHRR/Sounder(s)

Based on experience from:

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

MODIS examples from SSEC Direct Broadcast

Haze

Current GOES Visible Image

GOES-R resolves more details

Turbidity

Atoll Waters

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?