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GOES-R RISK REDUCTION (R3) ACTIVITIES Paul Menzel NESDIS Office of Research and Applications May 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)

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slide1

GOES-R RISK REDUCTION (R3) ACTIVITIES

      • Paul Menzel
      • NESDIS Office of Research and Applications
      • May 2004
slide2

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)

slide3

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 provides the necessary elements for early goes r utilization
R3 provides the necessary elements for early GOES-R utilization
  • 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,
  • champions of new opportunities in high positions,
  • well planned transitions from research demonstrations to operations, and
  • cost effective use of GOES-R for improved coastal ocean, weather & water, climate, and commerce applications
slide5

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

slide6

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!

GOES-R data and products will support all of NOAA’s four mission goals!

slide7

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.

slide8

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

slide9

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 addresses challenges of goes r data utilization
R3 addresses challenges of GOES-R data utilization
  • 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
slide12

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

slide13

Three Functions

HES, ABI, and CWI

slide14

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.

slide17

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

slide18

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

15 m CO2 Spectrum

Blue between-line Tbwarmer for tropospheric channels,colder for stratospheric channels

--tropopause--

Signature not available at low resolution

slide19

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

slide20

ABI Bands

Based on experience from:

MSG/AVHRR/Sounder(s)

MODIS/MTG/ Aircraft, etc

Current GOES Imagers

goes r coastal water imaging function
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
slide23

GOES-R resolves more details

Current GOES Visible Image

Turbidity

Haze

Atoll Waters

MODIS examples from SSEC Direct Broadcast

slide24

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 tocentennial time span)?

How are the oceanic ecosystems (open and coastal) changing? What portions are natural versus anthropogenic?