JPSS Products and Capabilities Lihang Zhou NOAA/NESDIS/STAR JPSS - - PowerPoint PPT Presentation

JPSS Products and Capabilities

Lihang Zhou

NOAA/NESDIS/STAR JPSS STAR (JSTAR) Program Manager

with contribution from

JSTAR Algorithm and Data Products Algorithm Team Leads/Members

1

Cal/Val Process Overview

Four Phases of Cal/Val:

• 1. Pre-Launch – Algorithm verification, sensor testing, and validation preparation
• 2. Early Orbit Check-out (first 30-90 days) – System Calibration & Characterization
• 3. Intensive Cal/Val (ICV) – Product Validation
• 4. Long-Term Monitoring (LTM); through life of sensors

LAUNCH

ICV EOC LTM

Build Team

Resource ID & Development Sensor Characterization Post

• Launch

Plan Dev.

• Alg. Assessment

& Verifications Cal/Val Tool Development Sens or Charar . &Calibration Quick - Look Analysis SDRs/EDRs SDR/EDR Alg. Tuning Estab . Sensor Stability SDR Validation Key EDR Validation Mission Integration Product Ops Viability Monitor Sensor Stability EDR Validation

PRE

• LAUNCH

LAUNCH

ICV EOC LTM

Build Team

Resource ID & Development Sensor Characterization Post

• Launch

Plan Dev.

• Alg. Assessment

& Verifications Cal/Val Tool Development Sens or Charar . &Calibration Quick - Look Analysis products Alg. Tuning Estab . Sensor Stability Sensor Data Validation Key EDR Validation Mission Integration Product Ops Viability Monitor Sensor Stability Products Validation

PRE

• LAUNCH

Product Operationalization

We Are Here for SNPP We Are Here for JPSS-1

2

Suomi NPP and JPSS-1 Instruments

Instrument Type Measurement ATMS - Advanced Technology Microwave Sounder

ATMS and CrIS together provide high vertical resolution temperature and water vapor information needed to maintain and improve forecast skill out to 5 to 7 days in advance for extreme weather events, including hurricanes and severe weather outbreaks

CrIS - Cross-track Infrared Sounder VIIRS – Visible Infrared Imaging Radiometer Suite

VIIRS provides many critical imagery products including snow/ice cover, clouds, fog, aerosols, fire, smoke plumes, vegetation health, phytoplankton abundance/chlorophyll

OMPS - Ozone Mapping and Profiler Suite

Ozone spectrometers for monitoring

• zone hole and recovery of

stratospheric ozone and for UV index forecasts

CERES - Clouds and the Earth’s Radiant Energy System

Scanning radiometer which supports studies of Earth Radiation Budget

3

3

VIIRS (28 EDRs)

RDR & SDR (for each of 22 bands) Albedo (Surface) Aerosol Particle Size Parameter Cloud Base Height Cloud Cover/Layers Cloud Effective Particle Size Cloud Optical Thickness Cloud Top Height Cloud Mask Ice Surface Temperature Imagery Aerosol Optical Thickness Cloud Top Pressure Cloud Top Temperature

KEY

Land Surface Temperature Quarterly Surface Type Sea Ice Characterization Ocean Color/Chlorophyll Sea Surface Temperature

OMPS-Nadir (2 EDRs) CERES1

RDR O3 Total Column EDRs Active Fires Snow Cover Surface Type Suspended Matter Vegetation Indices Green Vegetation Fraction Index Ocean Color/Chlorophyll Polar Winds Sea Surface Temperature Vegetation Health Index Suite

CrIS (5 EDRs)

RDR & SDR Carbon Dioxide Carbon Monoxide Methane Infrared Ozone Profile Outgoing Longwave Radiation

CrIS/ATMS (2 EDRs)

Atm Vertical Temperature Profile Atm Vertical Moisture Profile

ATMS (11 EDRs)

RDR, SDR, TDR Cloud Liquid Water EDRs: EDRs: EDRs: Imagery Land Surface Emissivity Land Surface Temperature Moisture Profile Rainfall Rate Sea Ice Concentration Snow Cover/Depth Snow Water Equivalent Temperature Profile Total Precipitable Water OMPS-N RDR & SDR O3 Nadir Profile EDRs:

RDR – Raw Data Record SDR – Sensor Data Record EDR – Environmental Data Record TDR – Temperature Data Record – Products with Key Performance Parameters

Bold – Indicates JPSS Ground System xDR Italics – Indicates NOAA Polar Legacy (ESPC) xDR

OMPS-L RDR2

The JPSS Program includes Ground System Support for the Metop, DMSP, GCOM, and Polar Free Flyer missions

OMPS-Limb2 AMSR2 (11 EDRs)3

RDR, SDR, TDR

December 12, 2013

This chart is controlled by JPSS Program Systems Engineering

JPSS-P Rev B

Cloud Liquid Water EDRs: Imagery Precipitation Type/Rate Precipitable Water Sea Ice Characterization Sea Surface Temperature Sea Surface Wind Speed Snow Cover/Depth Snow Water Equivalent Soil Moisture Surface Type

Notes:

1RDRs for the JPSS-2 Mission are contingent on NASA manifest of the Radiation Budget Instrument (RBI) 2Not applicable to JPSS-1; contingent on NASA manifest of OMPS-Limb on the JPSS-2 Mission 3Dependent on the Global Change Observation Mission (GCOM) provided by the Japan Aerospace Exploration Agency

JPSS Environment Products Production

4

Suomi NPP TDR/SDR Algorithm Maturity

C C C C C C C C C C C C C C

Sensor Beta Provisional Validated

CrIS February 10, 2012 February 6, 2013 March 18, 2014 ATMS May 2, 2012 February 12, 2013 March 18, 2014 OMPS March 7, 2012 March 12, 2013 April , 2015 VIIRS May 2, 2012 March 13, 2013 April 16, 2014 Beta

• Early release product.
• Initial calibration applied
• Minimally validated and may still contain significant errors (rapid changes can be expected. Version changes will not

be identified as errors are corrected as on-orbit baseline is not established)

• Available to allow users to gain familiarity with data formats and parameters
• Product is not appropriate as the basis for quantitative scientific publications studies and applications

Provisional

• Product quality may not be optimal
• Incremental product improvements are still occurring as calibration parameters are adjusted with sensor on-orbit

characterization (versions will be tracked)

• General research community is encouraged to participate in the QA and validation of the product, but need to be

aware that product validation and QA are ongoing

• Users are urged to consult the SDR product status document prior to use of the data in publications
• Ready for operational evaluation

Validated

• On-orbit sensor performance characterized and calibration parameters adjusted accordingly
• Ready for use in applications and scientific publications
• There may be later improved versions
• There will be strong versioning with documentation

5

JSTAR SDR Leads: Fuzhong Weng, Changyong Cao, Yong Han, Larry Flynn

VIIRS Imagery EDR

• Products
• 6 of 16 M-band visible/IR bands



High interest in all 16 M bands as EDRs from JPSS-1

• 5 of 5 (all) I-band visible/IR bands
• NCC (EDR) derived from DNB (SDR)
• DNB is also considered an Imagery product, often finding as

many uses as NCC because of better availability.



New Error-function-scaled DNB as possible NCC replacement

• File Format: HDF5/NetCDF4
• Maturity Status:
• Validated 04/23/2014 AERB approved
• JPSS-1 Cal/Val Plan/Timeline:
• Beta:

Launch + 3 months

• Provisional:

Launch + 6 months

• Validated:

Launch + 12 months

• Users:
• NWS (thru AWIPS-I and II)
• NRL (NexSat online imagery)

http://www.nrlmry.navy.mil/VIIRS.html

• NGDC (DNB cloud-free composites)

https://www.ngdc.noaa.gov/eog/viirs/download_monthly.h tml)

• National Ice Center (Great Lakes sector, supplied by NRL)
• Alaska (primary users of Direct Broadcast VIIRS)
• ICVS uses of Imagery for display

6

Color-enhanced I-band (375 m resolution) IR image

• f Typhoon Genevieve on 8 Aug 2014. Note the

extreme (and low-noise) detail of the very cold cloud tops, as well as the sloping walls of the typhoon eye.

• J1 Updates/Improvements:
• Reduce striping in DNB
• Possible Additional bands
• Improved Data latency

JSTAR Imagery Lead: Don Hillger

VIIRS Sea Surface Temperature (SST)

• Products
• ACSPO Sea Surface Temperature (Skin)
• File Format: NetCDF4
• Cal/Val Maturity Status:
• Validated 09/03/2014 Science Review
• Products are monitored online in near-real time
• iQUAM: in situ SST quality monitor

http://www.star.nesdis.noaa.gov/sod/sst/iquam

• SQUAM: SST Quality Monitor

http://www.star.nesdis.noaa.gov/sod/sst/squam

• Users:
• NOAA STAR (GEO/POLAR Blended L4)
• NOAA STAR (Coral Reef Watch)
• NOS (Chesapeake Bay Ecosystem analysis)
• NCDC (Reynolds SST L4)
• NASA JPL (JPL MUR L4)
• International Users:
• Canadian Met Centre (CMC L4)
• Australian Bureau of Meteorology (GAMSSA L4)
• UK Met Office (OSTIA L4)
• Japanese Met Agency (MGD L4)
• DMI, Denmark (DMI L4)
• EUMETSAT (EUMETCAST)
• JPL/PO DAAC (Archive)
• IFREMER, France (Odyssea L4)

7

• J1 Updates/Improvements:
• Fix overly conservative cloud mask in

coastal areas and dynamic parts of the

• cean
• Improve performance in the high latitudes
• Explore additional VIIRS bands M13, M14

potentially useful for SST

• Improve error characterization
• Implement de-striping operationally
• Generate new Level 3 ACSPO SST product

JSTAR SST Lead: Alex Ignatov

VIIRS Ocean Color EDR

Products:

• Normalized Water-leaving Radiance
• Chlorophyll-a
• Water Diffuse Attenuation Coefficient at 490

nm Kd (490)

• Total Suspended Sediment, water Turbidity

Cal Val Maturity: On March 27, STAR JPSS held a validation review the Multi- Sensor Level 1-2 (MSL12) Ocean Color EDR algorithm with VIIR) data for Validated Maturity. Users:

• NMFS (Surveys, Modeling)
• NWS (Ecosystem Forecasting, Modeling)
• NOS (HAB, Sanctuaries)
• OAR (Isoprene emissions, Ocean Acidification)

J1 Updates/Improvements: Algorithms improvements for coastal turbid and inland water are being developed

VIIRS chlorophyll climatology.

Example of destriping technique (Mikelsons et al. 2014) applied to water leaving radiances.

JSTAR Ocean Color Lead: Menghua Weng

VIIRS Green Vegetation Fraction (GVF)

• Products
• Daily Rolling Weekly 4-km GVF on a global grid in

NetCDF4 and GRIB2

• Daily Rolling Weekly 1-km GVF regional in

NetCDF4 and GRIB2

• Cal/Val Maturity Status:
• Declared operational on 02/12/2015
• Validation Maturity Assessment: Provisional
• J1 Updates/Improvements:
• Algorithm tuning updates
• Algorithm data handling updates
• Coefficient/LUT table updates
• Generation and incorporation of a SNPP VIIRS

GVF Climatology

• Users:
• NCEP/EMC
• STAR
• CLASS
• NASA SPoRT

9

4km resolution weekly global GVF (Sep 1-7, 2014)

1km resolution weekly regional GVF (Sep 1-7, 2014). Coverage Lat 90°N - 7.5°S, Lon 130°E - 30°E

JSTAR GVF Lead: Marco Vargas

VIIRS Vegetation Index

• Products
• Normalized Difference Vegetation Index (NDVI) from top-of-

atmosphere (TOA) reflectances

• Enhanced Vegetation Index (EVI) from top of canopy (TOC)

reflectances

• Normalized Difference Vegetation Index (NDVI) from top of

canopy (TOC) reflectances (New Product for JPSS-1)

• File format: HDF5
• Cal/Val Maturity Status:
• Validated 04/01/2015 AERB Approved
• Users:
• NCEP/EMC
• STAR
• CLASS
• USDA
• USGS
• University of Hawaii at Manoa
• The Climate Corporation
• University of Technology Sydney
• Japan Manned Space Systems Corporation
• VTT Technical Research Centre of Finland
• Gro Intelligence
• University of Copenhagen, Denmark

10

• J1 Updates/Improvements:
• Addition of a new dataset (TOC NDVI)
• Addition of a new Quality Flag byte (QF4)
• Improved definition of high quality of the

product

• Implementation of an EVI alternate (EVI2)

algorithm

• Generation of Level 3 products (spatial and

temporal composites)

TOA – NDVI February 11, 2015

JSTAR NDVI Lead: Marco Vargas

VIIRS Active Fire

• Products
• Contextual-thresholding detection algorithm

based on MODIS Collection 4 algorithm

• List of detections over cloud-free land
• Quality flags to characterize observing and

environmental conditions, input data quality and detection confidence

• File format: HDF5
• Cal/Val Maturity Status:
• Validated 09/04/2014 Science Review
• S-NPP Active Fire ARP was declared Operational

by SPSRB in Aug-2014

• Users:
• NESDIS Hazard Mapping System
• NOAA aerosol / air quality product suite
• NWS Fire Weather Program
• USDA Forest Service and other US agencies

through the National Interagency Fire Center

• A broad community of international users

11

• J1 Updates/Improvements:
• Include additional output: Fire Radiative

Power (FRP)

• Provide a 2D array of values representing

the fire and other relevant thematic classes

• f each pixel. This is a new attribute to

describe land etc for each pixel.

• Provide global coverage (include water)
• Schedules/Timeline:
• The S-NPP replacement algorithm is

planned to be implemented in NOAA’s NDE system by late Summer 2015

July 13 2014, NW Canada

JSTAR Active Fire Lead: Ivan Csiszar

VIIRS Land Surface Temperature (LST)

• Products
• Land Surface Temperature



A single swath granule dataset with the dimension of 768x3200



A 4-swath aggregated granule dataset with the dimension

• f 3072 X 3200
• File format: HDF5
• Cal/Val Maturity Status:
• Validated 03/25/2015 AERB Approved
• Users:
• NCEP/EMC
• NOAA National Weather Service Environmental Modeling Center
• USDA Agricultural Research
• USDA Forest Service
• NOAA/NESDIS Center for Satellite Applications and Research
• NOAA/NESDIS National Climate Data Center
• University of Maryland
• Army Research Lab
• International Users:
• EUMETSAT LSA SAF LST group
• ESA/ESRIN, Italy
• University Of Edinburgh, UK
• OBSPM, and LSCE, France
• Universitat de les Illes Balears, Spain
• eLEAF, The Netherlands
• Centre for Ecology and Hydrology, UK
• Institute of Geodesy and Cartography, Poland

12

• J1 Updates/Improvements:
• Water Vapor correction
• Angular correction
• Current LST algorithm shows high

dependency on the quality of VIIRS surface type product. While the VIIRS Surface type data is changed to be annual gridded data, significant impact to the LST retrieval is

• expected. Therefore, emissivity explicit

algorithm is necessary to account for the emissivity change within a surface type. VIIRS Global LST (daytime): 02/01/2015

JSTAR LST Lead: Bob Yu

VIIRS Surface Albedo

• Products
• Land Surface Albedo (LSA)
• Ocean Surface Albedo (OSA)
• Sea-Ice Surface Albedo (SSA)
• Only LSA and SSA are validated and maintained in

the current VIIRS Albedo release

• File format: HDF5
• Cal/Val Maturity Status:
• Validated 03/25/2015 AERB Approved
• Users:
• NOAA National Weather Service Environmental

Modeling Center

• USDA Agricultural Research
• USDA Forest Service
• NOAA/NESDIS Center for Satellite Applications

and Research

• NOAA/NESDIS National Climate Data Center
• University of Maryland
• Army Research Lab

13

• J1 Updates/Improvements:
• Update LUT of regression coefficients for

estimating sea ice Albedo

• Develop a separate LUT for snow pixels and
• ther major land surface types
• Implement a temporal filtering to improve

both quality and continuity

• Propose a framework to generate gridded

data set of LSA VIIRS Land Surface Albedo Map, April 3 2012

JSTAR Albedo Lead: Bob Yu

VIIRS Surface Type

• Products
• VIIRS Surface Type (granule level)
• VIIRS Quality Surface Type IP (QST IP, Global

Gridded Surface Type Map)

• File format: HDF5
• Cal/Val Maturity Status:
• Validated 04/01/2015 AERB Approved
• Users:
• Modeling studies



Land surface parameterization for GCMs (e.g. NCEP Noah LSM)



Biogeochemical cycles



Hydrological processes

• Carbon and ecosystem studies



Carbon stock, fluxes



Biodiversity

• Downstream products



Land surface temperature, cloud mask, aerosol products, other products require global land/water location information

14

• J1 Updates/Improvements:
• Support vector machines (SVM) may replace

the decision tree (DT) algorithm for higher reliability/stability

• Multiple year classification metrics are

expected to produce more stable Surface Type map

• Schedules/Timeline:
• SVM product evaluated

Dec-2015

• New global surface type IP (QST IP) delivered

to IDPS Dec-2015

S-NPP VIIRS Surface Type IP: Global Surface Type Map

JSTAR Surface Type Lead: Jerry Zhan

VIIRS Snow and Ice (1/2)

• Products
• Sea ice characterization



Currently this is an age category: no ice, new/young ice, other ice

• Sea Ice concentration



Fractional coverage of ice in each pixel

• Ice surface temperature (IST)



Radiating temperature of the surface (ice with

• r without snow)
• Snow cover



Binary snow cover



Fractional snow cover (currently 2x2 averages

• f binary mask)
• File format: HDF5
• Cal/Val Maturity Status:
• Snow Cover: Binary Map



Validated 01/08/2014 Science Review

• Snow Cover: Fractional Snow Cover



Provisional 12/20/2013 AERB Approved

• Ice Surface Temperature



Validated 01/08/2014 Science Review

• Sea ice characterization



Provisional 12/20/2013 AERB Approved

15

• J1 Updates/Improvements:
• It has been recommended that the following

IDPS products be replaced by their more robust and accurate counterparts that are being implemented in NDE:



Sea Ice Characterization (IDPS)  Sea Ice Thickness (NDE)



Snow Fraction (IDPS; average of binary cells)  Snow Fraction (NDE; subpixel snow fraction)

• Both Sea Ice Thickness and Snow Fraction

algorithms should be Operational in 2016

• Users
• NIC, National/Naval Ice Center
• NWS, National Weather Service, including

the Alaska Ice Desk

• NOHRSC - National Operational Hydrologic

Remote Sensing Center

• NSIDC, National Snow Ice Data Center
• STAR, Center for Satellite Applications and

Research

• NASA Goddard Space Flight Center

Hydrological Sciences Branch

JSTAR Cryosphere Lead: Jeff Key

VIIRS Snow and Ice (2/2)

16

Snow Cover March 23, 2013

snow cloud land No data

S-NPP VIIRS

March 23, 2013

Ice Surface Temperature 2/27/2012

VIIRS Ice Thickness

VIIRS Clouds (1/2)

• Products
• Cloud Detection – 4 level mask and probability (detect the

probability of presence of cloud)

• Cloud Typing – Initial qualitative classification of cloudy pixels

into several cloud types

• Cloud-top Temperature – Temperature of highest cloud in

column

• Cloud-top Pressure – Pressure of highest cloud in column
• Cloud-top Height – Height of highest cloud in column
• Cloud Emissivity (aka Effective Cloud Amount) – Emissivity at

11 micron

• Cloud IR Microphysical Index – Cloud microphysical index

derived from 11 and 12 micron

• Cloud Optical Depth (Day) – Optical of cloud based on solar

reflectance

• Cloud Particle Size (Day) – Effective radius of cloud-top size

distribution based on solar reflectance

• Cloud Optical Depth (Night) – Optical of cloud based on SWIR

and IR emission

• Cloud Particle Size (Night) – Effective radius of cloud-top size

distribution based SWIR and IR emission

• Cloud Liquid Water Path – Integrated Cloud Water Path for

the whole column for liquid phase clouds

• Cloud Ice Water Path – Integrated Cloud Water Path for the

whole column for ice phase clouds

• Cloud Base Height – Height of ceiling of lowest cloud in the

column

• Cloud Cover Layers – cloud fraction at selected vertical levels

17

• Cal/Val Maturity Status:
• Cloud Mask:

 Validated 03/05/2014 AERB Approved

• Cloud Top (CTH,CTT,CTP), Cloud Optical

Thickness (daytime), Cloud Effective Particle Size, Cloud Base Height:

 Validated 09/03/2014 Science Review

• Cloud Optical Thickness (nighttime), Cloud

Cover Layers:

 Provisional 08/20/2014 AERB Approved

• Users:
• NCEP/EMC
• NWS – AWIPS Imagery from CIMSS
• NWS – Aviation Weather Center
• NWS – Composited cloud products over Alaska
• NOAA/ESRL – Cloud product assimilation for

Global Model

• NWP – Cloud products for VIIRS Polar Winds
• Climate – VIIRS products will be included in

future PATMOS-x releases and hosted at NCDC

• File Format:
• HDF5 (IDPS Product: Cloud Mask)
• NetCDF4 (NDE Products: Cloud Mask, Cloud

Properties)

JSTAR Clouds Lead: Andy Heidinger

VIIRS Clouds (2/2)

18

VIIRS Cloud Top Pressure

• Images show VIIRS DB data from UW/CIMSS processed through the NDE Cloud Algorithms in

CSPP/CLAVR-x.

• Image on the left is a false color image (R=M5, G=M7, B=M15(rev)). March 8, 2015. Orbit 17414.
• Cloud Top Pressure (right) is important for Aviation and used in the VIIRS Winds Application.

VIIRS Aerosol

• Products
• Aerosol Optical Thickness (AOT, 10 M Bands +

550 µm)

• Aerosol Particle Size Parameter (APSP)
• Suspended Matter (SM)
• File format: HDF5 (IDPS), NetCDF4 (NDE)
• Cal/Val Maturity Status:
• AOT, APSP:



Validated 12/22/2014 AERB Approved

• SM:



Beta 06/26/2013 AERB Approved

• Users:
• NWS NCEP
• NWS Alaska
• NWS Western Region
• NRL
• OSPO/SAB
• EPA

19

Global Gridded 550-µm AOT, 11/12/2013

• J1 Updates/Improvements :
• Aerosol Optical Thickness:

 Adding capability to retrieve AOT over bright

surface

 Improve data filtering

• Suspended Matter :

 Replace IDPS SM algorithm by JPSS Risk Reduction

(RR) SM algorithm for dust and smoke detection

 JPSS RR Volcanic Ash JSTAR Aerosol Leads: Shobha Kondragunta and Istvan Laszlo

OMPS Ozone

• Products
• OMPS Nadir Mapper Total Ozone – Daily Global Maps

 Total Column Ozone  Total Column SO2  UV Absorbing Aerosol Index  Effective UV Reflectivity  Total Column and Tropospheric NO2

• OMPS Nadir Profiler Ozone Profile

 Vertical ozone profiles in Dobson units +

Averaging Kernels

 Polar Mesospheric Clouds and Mg II Solar Index

• OMPS Limb Profiler Ozone Profile

 Vertical Ozone profiles in Dobson units  Vertical Aerosol Optical Depth profiles  Polar stratospheric clouds, rocket and meteor

plumes, and upper atmosphere temperature

profiles

• File format: HDF5 (IDPS, TC & NP); NetCDF4 (Limb Profile)
• Cal/Val Maturity Status:
• OMPS NP & TC:



Validated 09/03/2014 Science Review. Pending on OMPS SDR

20 Comparison of OMPS Total Column Ozone and Limb Profile Ozone, Outside and Inside the Antarctic Ozone Hole

Figure courtesy C. Seftor, NASA GSFC

• J1 Updates/Improvements :
• OMPS Nadir Mapper Total Ozone (TC):



Adapt V8TOz code to retrieve products for up to 105 CT by15 Along-Track FOV measurements

• OMPS Nadir Profiler Ozone Profile (NP):



Glueware aggregator to convert 5x5 NP FOVs to 1x1 NP FOVs and to match up and convert 15x15 NM FOVs to 1x1 NP FOVs



Adapt V8Pro code to retrieve products for up to 5 CT and 5 Along-Track FOV measurements

JSTAR Ozone Lead: Larry Flynn

NESDIS Unique CrIS-ATMS Product System (NUCAPS) Products

• Products
• Temperature profile (AVTP)
• Water vapor profile (AVMP)
• CrIS Ozone profile (O3)
• Outgoing Longwave Radiation (OLR)
• Trace Gas (CO, CO2, CH4, SO2, N2O, HNO3)
• File format: NetCDF4
• Cal/Val Maturity Status:
• Temperature/Water Vapor Profile:



Validated 09/03/2014 Science Review

• Trace Gas:



Operational: Running in current NDE



Validation Maturity Assessment: Provisional

• OLR: In development



• Delivered. Expected Operational: Nov/Dec-2015



Validation Maturity Assessment: Provisional

• Users:
• NOAA CLASS
• NOAA AWIPS-II
• FNMOC
• Direct broadcast
• NOAA/NCEP/CPC
• NOAA/OAR
• Basic and applied geophysical science

research/investigation

21

• J1 Updates/Improvements:
• Extend NUCAPS for CrIS full spectral data
• Develop/update trace gaseous products
• Generate regression coefficients using CERES OLR
• Improve surface emissivity
• Enhance microwave retrieval
• Schedules/Timeline:
• CrIS full spectral channel selection for NWP and NUCAPS

Oct-2015

• CrIS Full Spectral Data in Sounding System

Oct-2015

• Trace Gas (CO, CO2, and CH4) Algorithm Tuning, Validation,

and Verification June-2016

• Enhancement of microwave retrieval in NUCAPS

June-2016

NUCAPS (left) vs ECMWF (right), T & H2O

JSTAR NUCAPS Lead: Mark Liu

SNPP Maturity Status (Will Include all NESDIS Unique Products for JPSS-1)

JPSS-1 Key Performance Parameters (KPPs) Nominal Cal/Val Timeline (Draft)

Launch Operations Post-launch Testing (PLT) 3-months

L + 3 m

ATMS

Other KPPs

Beta Validated LTM

• Beginning of each color represents when product enters a given validation stage.
• Dependencies will be built into the schedule for each downstream EDR product.
• This is a draft timeline, detailed schedule will be available In summer 2015.

3-months 3-months 3-months 3-months 3-months 3-months

L + 6 m L + 12 m L + 21 m

Provisional 23

• Most of the operational Suomi NPP products have reached Validated maturity
• level. SNPP Product evaluation and updates are continuing
• Long-term monitoring and reactive maintenance
• Replacement and upgrade of current Suomi NPP algorithms with NOAA

enterprise algorithms

• STAR has existing S-NPP experience on the development and utility of test

data sets, diagnostic tools, and data analysis.

• Leveraging this experience, and improved knowledge of the pre-launch

characterization of the J1 instruments, the JSTAR teams work with JPSS Algorithm Management Project are ready to accelerate the Cal/Val activities for JPSS-1 sensor and data products.

• STAR JPSS Science Team Annual Meeting is scheduled for August 24-28,

2015, NCWCP, College Park, MD. SNPP lessons learned and plans for JPSS-1 Cal Val will be presented in details.

Summary and Conclusions