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GOES-R Field Campaign Workshop, Apr 8-9, 2015
GOES-R Algorithm Working Group (AWG) GOES-R Algorithm Working Group (AWG) Planning and Participation in GOES-R Field Planning and Participation in GOES-R Field
Campaign ActivitiesCampaign Activities
Jaime DanielsJaime Daniels
Center for Satellite Applications and Center for Satellite Applications and
ResearchResearchNational Environmental Satellite Data and Information Surface
National Oceanic and Atmospheric Administration
1
Contributions from AWG Team Leads and Team Members
GOES-R Field Campaign Workshop, Apr 8-9, 2015
OutlineOutline• The Algorithm Working Group (AWG): A Brief
Introduction
• AWG Level-2 Product Validation Methods, Reference Data, and Activities
• AWG Team Participation in Targeted Field Campaigns
2
GOES-R Field Campaign Workshop, Apr 8-9, 2015
OutlineOutline• The Algorithm Working Group (AWG): A Brief
Introduction
• AWG Level-2 Product Validation Methods, Reference Data, and Activities
• AWG Team Participation in Targeted Field Campaigns
3
GOES-R Field Campaign Workshop, Apr 8-9, 2015
GOES-R Algorithm Working GroupGOES-R Algorithm Working Group
• End-to-End Capabilities– Instrument Trade Studies
– Proxy Dataset Development
– Algorithm Development and Testing
– Product Demonstration Systems
– Development of Cal/Val Tools
– Integrated Cal/Val Enterprise System
– Radiance and Product Validation
– Algorithm and application improvements
– User Readiness and Education
4
GOES-R Field Campaign Workshop, Apr 8-9, 2015
STAR GOES-R AWG PM
Jaime DanielsManagement Support
Tess Valenzuela (Budget/Schedule)
Algorithm Integration
Walter Wolf
Algorithm Integration
Walter Wolf
Proxy Fuhzong Weng
Proxy Fuhzong Weng
Radiation Budget Istvan Laszlo
Radiation Budget Istvan Laszlo
Land Bob YuLand
Bob Yu
CryosphereJeff Key
CryosphereJeff Key
Ocean DynamicsEileen Maturi
Ocean DynamicsEileen Maturi
SSTAlexander Ignatov
SSTAlexander Ignatov
LightningSteve Goodman
LightningSteve Goodman
Hydrology Bob Kuligowski
Hydrology Bob KuligowskiAerosols
Shobha KondraguntaAerosols
Shobha Kondragunta
Winds Jaime Daniels
Winds Jaime Daniels
Imagery Tim SchmitImagery
Tim Schmit
SoundingsTim SchmitSoundingsTim Schmit
AviationKen Pryor
Mike Pavolonis
AviationKen Pryor
Mike Pavolonis
CloudsAndy Heidinger
CloudsAndy Heidinger
Cal/Val (Sensor) Fred Wu
Cal/Val (Sensor) Fred Wu
Algorithm Working GroupAlgorithm Working Group
Team leads are STAR Scientists
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
GOES-R ProductsGOES-R ProductsAdvanced Baseline Imager (ABI)
Aerosol Detection (Including Smoke and Dust)Aerosol Optical Depth (AOD)Clear Sky MasksCloud and Moisture ImageryCloud Optical DepthCloud Particle Size DistributionCloud Top HeightCloud Top PhaseCloud Top PressureCloud Top TemperatureDerived Motion WindsDerived Stability IndicesDownward Shortwave Radiation: SurfaceFire/Hot Spot CharacterizationHurricane Intensity EstimationLand Surface Temperature (Skin)Legacy Vertical Moisture ProfileLegacy Vertical Temperature ProfileRadiancesRainfall Rate/QPEReflected Shortwave Radiation: TOASea Surface Temperature (Skin)Snow CoverTotal Precipitable WaterVolcanic Ash: Detection and Height
Geostationary Lightning Mapper (GLM)
Lightning Detection: Events, Groups & Flashes
Space Environment In-Situ Suite (SEISS)
Energetic Heavy Ions
Magnetospheric Electrons & Protons: Low Energy
Magnetospheric Electrons: Med & High Energy
Magnetospheric Protons: Med & High Energy
Solar and Galactic Protons
Magnetometer (MAG)
Geomagnetic Field
Extreme Ultraviolet and X-ray Irradiance Suite (EXIS)
Solar Flux: EUVSolar Flux: X-ray Irradiance
Solar Ultraviolet Imager (SUVI)
Solar EUV Imagery
Baseline ProductsBaseline Products
Level-2 products outlined in red
6
GOES-R Field Campaign Workshop, Apr 8-9, 2015 7http://www.goes-r.gov/products/baseline.html7
GOES-R Field Campaign Workshop, Apr 8-9, 2015
OutlineOutline
• The Algorithm Working Group (AWG): A Brief Introduction
• AWG Level-2 Product Validation Methods, Reference Data, and Activities
• AWG Team Participation in Targeted Field Campaigns
8
GOES-R Field Campaign Workshop, Apr 8-9, 2015
Overarching Product Science Overarching Product Science Validation MethodsValidation Methods
• Product inspection Visualization of product and/or any output fields in the product, intermediate, and/or
diagnostic files using in-house software tools Provides a quick qualitative assessment of product performance
• Comparison to reference/correlative/ground truth data Collocate product and applicable reference/correlative/ground truth datasets and
compute quantitative statistics (accuracy, precision, etc) Visualization of product and/or any output fields in the product, intermediate, and/or
diagnostic files together with reference/correlative/ground truth data using in-house software tools
Provides a quantitative assessment of product performance. Focuses on assessing and characterizing product quality (ie., accuracy and precision) that needs to be conveyed to the user community.
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Overarching Product Science Overarching Product Science Validation ProcessValidation Process
Diverse earth (atmospheric & surface) and space weather conditions are represented over space, time and measurement range.
Compute estimates of on-orbit product performance
Characterize errors
Goal: Determine if product meets success criteria
Product Generation
Validate (Inspect and/or Compare with Reference/ Ground Truth)
Algorithm
Approach for validating performance of products derived from GOES-R data and Approach for validating performance of products derived from GOES-R data and correlative data sources…correlative data sources…
Validation Tools
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Reference/”Ground Truth” Reference/”Ground Truth” Data SourcesData Sources
Aeronet Stations
Aerosol Optical Depth
Radiosondes
Winds,Temperature, Moisture, Stability
CALIPSO, CLOUDSAT Clouds, Icing
NWP Analyses Winds, Temperature, Moisture
Bouys, Ships
SST
SURFRAD, ARM LST, Radiation
Rain Guages Precipitation
Sfc Snow Reports, NESDIS IMS
Snow
National Lightning Detection Network (NLDN)
Lightning
Pilot, aircraft Reports Icing,Turbulence, winds
Ground-based Ozone Ozone
Validation teams use a wide variety of Reference/“Ground Truth” datasets to Validation teams use a wide variety of Reference/“Ground Truth” datasets to assess and validate product performance.assess and validate product performance.
GOES EPS and HEPADProtons, Alpha Particles and Electrons
NASA SDO Solar Imagery
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
• Types – Primarily a mix of in-house developed software & COTS software– In-house developed software includes: visualization, collocation, and
analysis tools– Types of COTS software include: McIDAS-X, McIDAS-V, IDL, GrADS
• Current State– Mature. – AWG teams have been working to develop these tools since 2006.– Nearly all tools are in a working state with updated versions being
developed.
Status of AWG L2 Product Status of AWG L2 Product Validation ToolsValidation Tools
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Product Validation ToolsProduct Validation ToolsAWG teams have developed a cadre of product validation tools as part
of their ongoing product validation activities
Significance: The tools will enable the routine monitoring of L2 product performance and for “Deep-dive” assessments and analysis of products to resolve any issues/anomalies that may arise
Clouds
LST
Aerosols
13
GOES-R Field Campaign Workshop, Apr 8-9, 2015
OutlineOutline• The Algorithm Working Group (AWG): A Brief
Introduction
• AWG Level-2 Product Validation Methods, Reference Data, and Activities
• AWG Team Participation in Targeted Field Campaigns
14
GOES-R Field Campaign Workshop, Apr 8-9, 2015
• AWG product application teams asked to identify where gap filling measurements would enable more complete validation for L2 products they are responsible for
• The Unmanned Aircraft Systems (UAS) capability has had strong support from a number of the AWG product teams (Radiation Budget, Land, Aerosol)
• AWG has also provided critical support since early discussions with the NOAA UAS team through numerous TIMs and support in the development of a draft GOES-R UAS Near Surface Science Requirements document as well as an attendant CONOPS plan
AWG Team Participation in AWG Team Participation in Targeted Field CampaignsTargeted Field Campaigns
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
AWG Team Participation in AWG Team Participation in Targeted Field CampaignsTargeted Field Campaigns
• Land Team (Bob Yu)– Land surface temperature– Fire
• Radiation Budget Team (Istvan Laszlo)– Shortwave radiation
• Cloud Team (Andy Heidinger)– Cloud-top type, cloud-top height,
cloud optical depth, cloud particle size, liquid water path, ice water path
• Aerosols/Air Quality/Atmospheric Chemistry Team (Shobha Kondragunta)– Aerosol optical depth (AOD), but
more importantly, PM2.5 concentrations derived from retrieved AOD
16
• Lightning Team (Steve Goodman)
• Lightning Detection
In collaboration with external partners…
GOES-R Field Campaign Workshop, Apr 8-9, 2015
Land Surface Temperature
Slides courtesy of Bob Yu
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GOES-R Field Campaign Workshop, Apr 8-9, 2015 18
• Components of LST Validation In-situ measurement comparisons and analyses Cross-satellite comparisons and analyses Successful applications– users promotion
• Strategy of In-situ measurement comparisons and analyses Existing ground station observations (e.g. SURFRAD Network), serve as long-term validation data source
Field campaign data plays three important roles
High quality observations for direct comparison and analysis
Calibrating co-site ground station observations
Characterizing the heterogeneity of the ground station site
Towards the field campaign readiness
Platform: Low altitude, small Unmanned Aircraft Systems (UAS)
Instrument readiness: Accurate infrared radiometers cover ABI bands
Site selection: Better to cover SURFRAD/CRN station
Data processing and algorithms: noise filtering, spatial
characterization, calibration to station data, etc.
Coordination with the Field Campaign Team
Towards Field Campaign Towards Field Campaign for LST Validationfor LST Validation
Down-looking PIR at 8 meter height from the ground
UP-looking PIR
Diffuse Radiometer
Down-looking PIR on the towerAt 8-m from ground
Thermometer
Anemometer
GOES-R Field Campaign Workshop, Apr 8-9, 2015
Fire
Slides courtesy of Ivan Csiszar
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
• In general, for proper validation we need the mapping of the thermal conditions at high resolution within the entire pixel footprint. This needs to be done with sensors that have the proper bands (ideally ~ 4 microns, but there is some flexibility here), and simultaneously with the satellite overpass due to the very dynamic nature of fires.
• These data help determine detection probabilities as a function of sub-pixel fire characteristics. As a minimum, statistically we can determine the probability of detection as a function of sub-pixel fire size or fraction. If we can also measure unsaturated thermal radiance we can also do this in a two-dimensional space - detection probability as a function of fire size and temperature (or intensity).
• Getting these data is very hard and opportunistic. Realistically, we cannot sample the entire size/temperature space, but rather, use such reference data as anchor points to confirm simulated performance statistics.
Benefits to Fire Product Algorithm
VIIRS 375m Fire data
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Shortwave Radiation
Slides courtesy of Istvan Laszlo
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Benefits to AOD and SRB Algorithms
• Better characterization of surface and atmospheric state– Can test/diagnose algorithm* under
optimal conditions (many normally remote-sensed, modeled or climatological parameters are “measured”)
– Can get information on spatial variability of surface and atmosphere conditions, and on L2 parameters on a scale approaching/comparable to that of an ABI pixel
• Measurements* of AOD and SRB at locations (e.g., open ocean) where reference data are not readily available.*NOTE: data are limited in space and time; it is understood that it will not provide true measure of algorithm performance and product quality
Su
rface?
Cloud?
Aero
sol?
Gas?
SURFRAD+ sites
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Plan for Using Field-Campaign Data Plan for Using Field-Campaign Data
• Use field-campaign AOD and DSR to evaluate ABI retrievals of like quantities
• Use field-campaign AOD, surface reflectance (directional and spectral), water vapor profile, sky condition (cloudiness), etc., to identify sources of errors.– Retrievals will be performed by replacing derived,
modeled or climatological data with field-campaign data – one at a time, and
– Results will be compared to those from “normal” retrievals.
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
AOD ExampleAOD Example
• UAV measurements can be used to check components of the ocean model values
• UAV measures sum of Terms 1 and 2 (approximately).
• “Integration” of directional UAV measurements gives the sum of all four Terms.
• Dividing the “integrated” value by the separately measured downward irradiance gives the albedo needed in AOD retrieval over land (and in SRB retrieval).
Term 2
diffuse indirect out
•Ocean surface reflectance in ABI AOD algorithm is the sum of
1. water-leaving reflectance2. whitecap reflectance3. bi-directional reflectance
•Bi-directional reflectance is modeled as the sum of 4 components.
Term 1
direct indirect out
Term 3
direct indiffuse out
Term 4
diffuse indiffuse out
+++
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
SRB ExampleSRB Example
• Large negative downward shortwave radiation (DSR) bias at Cape Cod during an ARM field campaign in 2012.
• Deep-dive analysis: On average, retrieved AOD > ground-measured AOD; retrieved spectral reflectance albedo < ground-albedo derived from spectral values => retrieved DSR < ground-measured DSR.
DSR is severely underestimated on this easy, dominantly clear-sky day.Why? Too small surface albedo? Too large AOD?
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
UAV Measurements Quantify Spatial UAV Measurements Quantify Spatial Representativeness of Station DataRepresentativeness of Station Data
• Illustration of problem
• UAV data can help
• Satellite “sees” larger area than downward looking instrument does.
• Upward looking instrument “sees” radiation from an entire hemisphere.
• This represents inconsistency unless atmosphere and surface are homogeneous (uniform)
• Deployment of UAV at several different locations within the satellite footprint can characterize degree of uniformity within footprint.
• Ideally, this should be done for all reference (e.g. SURFRAD) sites in different seasons.
Satellite footprint on ground
Box size of satellite footprint
station
UAV
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Cloud Products
Slides courtesy of Andy Heidinger
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Use of LIDAR for Cloud ValidationUse of LIDAR for Cloud Validation
Airborne lidars provide higher spatial resolution data than spaceborne.
Our CALIPSO CALIOP tools have been applied to ground-based lidar during DC3
Provides almost ideal validation source of cloud vertical profiles when viewed from above.
Depolarization also provide cloud-top phase.
Multi-layer detection component of cloud type and height algorithms can also be validated.
Example Comparison of CALIPSO and ACHA for one AQUA/MODIS Granule .
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Use of Surface Based Radiometers for Daytime Cloud Optical and Microphysical
Properties Validation (DCOMP)
• The Solar Spectral Flux Radiometer (SSFR) is a shortwave spectrometer operated by U. Colorado / LASP.
• During CALNEX 2010 it was operated on a ship looking up.
• It provides retrievals of DCOMP cloud properties using radiation travelling through the cloud (not reflected off the top like DCOMP).
• This provides a more independent validation and can be accomplished with any upward looking well-calibrated radiometers.
• AVIRIS also is useful for this, if available.
Red lines are the DCOMP Error Bars
Liquid Water Path (lwp) is an option 2 DCOMP product
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Aerosols
Slides courtesy of Shohba Kondragunta and Xinrong Ren (UMD)
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Benefits to Aerosol Algorithms
• The field campaign focus is NOT to validate Aerosol Optical Depth (AOD) as this can be done sufficiently with AERONET observations.
• Rather, the field campaign focus is to evaluate surface PM2.5 derived from AOD
• AWG Aerosol Team has partnered with the University of Maryland (UMD) to do the flights and get aerosol vertical profile and Single Scattering Albedo (SSA) information.
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Evaluating Surface PM2.5 Derived from Evaluating Surface PM2.5 Derived from Retrieved Aerosol Optical DepthRetrieved Aerosol Optical Depth
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
UMD Research Aircraft Capabilities for UMD Research Aircraft Capabilities for GOES-R ValidationGOES-R Validation
Results from the summer 2013 flights over the Eastern Shore
0 0.1 0.2 0.3 0.40.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Aircraft integrated AOD
AO
D V
IIR
S
y = 1.07x + 0.129 r2 = 0.61
0 0.05 0.1 0.15 0.2 0.25 0.3 0.350
5
10
15
20
25
30
35
Aircraft Integrated AOD
[PM
2.5]
(g
m-3
)
y = 118.4x + 1.05 r2 = 0.87
0 0.1 0.2 0.3 0.4 0.50
5
10
15
20
25
30
VIIRS AOD
[PM
2.5]
(g
m-3
)
y = 69.0x - 5.00r2 = 0.57
GPS Position (Lat, Long, Altitude)
Met (T, RH, P, wind speed/direction)
Trace gases:O3: UV Absorption, modified TECOSO2: Pulsed Fluorescence, modified TECO
CH4/CO2/CO: Cavity Ring Down, PicarroNO2: Cavity Ring Down, Los Gatos
Aerosol Optical Properties:Scattering: bscat (@450, 550, 700 nm),
Nephelometer
Absorption: bap (565 nm), PSAPBlack Carbon: Aethalometer (7 wavelengths)
Data Acquisition: 1 sec
Aerosol Inlet
Gas Inlet
Met Sensors
(see the poster Ren et al.)
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
-78 -77.5 -77 -76.5 -76 -75.537.8
38
38.2
38.4
38.6
38.8
39
39.2
39.4
39.6
Longitude ( )
La
titu
de
( )
Extinction (Mm-1)
0
10
20
30
40
50
60
Extinction = Absorption + Scattering and can be measured at 3 wavelengths
0 10 20 30 40 500
1000
2000
3000
4000
5000
6000
7000
Ext (Mm-1)
Altitu
de
(ft)
0 10 20 30 40 500
1000
2000
3000
4000
5000
6000
7000
Ext (Mm-1)A
ltitu
de
(ft)
0 10 20 30 40 500
1000
2000
3000
4000
5000
6000
7000
Ext (Mm-1)
Altitu
de
(ft)
Extinction measured during 3 spirals
Results from a FLAGG-MD Winter 2015 flight on 02/20/2015
UMD Research Aircraft for Measuring UMD Research Aircraft for Measuring Urban EmissionsUrban Emissions
34
GOES-R Field Campaign Workshop, Apr 8-9, 2015
GLM Lightning Product
Slide information courtesy of Steve Goodman
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GOES-R Field Campaign Workshop, Apr 8-9, 2015
Benefits to GLM Lightning Detection Algorithm
• Validation of GLM flash detection efficiency: collect coincident and collocated high altitude data over thunderstorms with the Fly’s Eye GLM Simulator (FEGS*):
Minimum Collection Set: Over-flights of thunderstorms
over well characterized total lightning super sites. Emphasis on large scale convection such as Mesoscale Convective Systems (MCSs) from pre-storm through entire evolution (include all times of day & other storm types)
Secondary Collection Set: Over-flights of thunderstorms at other locations (day / dawn or dusk/ night; high / low latitudes; land / ocean; various storm types / regimes)
• Validation of GLM flash location & time-stamp accuracy, and INR
• Validation of optical energy calibration for the GLM product (lightning, which consists of events, groups, and flashes)
SURFRAD+ sites
36
GOES-R Field Campaign Workshop, Apr 8-9, 2015
SummarySummary• AWG team participation identified along with their associated Level-2
products whose validation can benefit from GOES-R Field Campaign measurements
• AWG teams will perform data analysis at their respective local compute facilities– Leverage many of the validation tools already developed as part of AWG program– Product reprocessing capability exists and can be exercised
• AWG teams involved in this will perform data analysis and document their analysis results (Expect some extra cost beyond current funding)
• For Discussion– Who/where/how will data archival and management be done?
37
GOES-R Field Campaign Workshop, Apr 8-9, 2015 38
BACKUP
GOES-R Field Campaign Workshop, Apr 8-9, 2015 39
Sensor characterization - Radiometric calibration
- Geolocation/navigation
Post-Launch Tests (PLT) and engineering tests (compliance)
Established sensor stability QC/QA processes in place
Proxy data generation Calibration Processing;
Analysis of L1b products
Sensor characterization - Radiometric calibration
- Geolocation/navigation
Continuous assessment & monitoring, trend analysis of product quality
Algorithm assessment and verification
Quick look analysis of L2 products; comparisons to NWP model/analyses
Finalize L2 algorithm tuning and testing; Establish L2 product stability
Algorithm improvements
Determination of validation strategies, including identification and acquisition of “ground-truth”/reference datasets
Work to establish sensor stability;
Work to establish L1b and L2 product stability;
L1b and L2 algorithm testing and tuning
L1b/L2 product validation processes in place;
L1b and L2 product validations
Full and continuous data release to the user community
Cal/Val tool development Establish routine validation processes
Increasing data release to the user community
Cal/Val tool improvements
Development of L1b & L2 Cal/Val Plans
Data released to users, but data is understood to be non-operational
Cal/Val tool improvements Data Archival
Data Archival Data Archival
Early Orbit Check-out
Long Term Monitoring & OperationsIntensive Cal/Val Pre-launch Cal/Val
Pre-Launch Post-LaunchLaunch
Launch
Timeline ~6 months
Calibration/ValidationCalibration/Validation PhasesPhases
39