land team chair: yunyue (bob) yu noaa/nesdis/star

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GOES-R AWG Product Validation Tool Development

Land Baseline Products

Land Surface Temperatureand

Fire Detection and Characterization

Land Team Chair: Yunyue (Bob) Yu NOAA/NESDIS/STAR

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Land Surface Temperature

Application Team

Yunyue (Bob) Yu (STAR)Dan Tarpley (Short & Associates)Hui Xu, Xiao-long Wang (IMSG)

Rob Hale (CIRA)Kostya Vinnikov (CICS)

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LST Products

● The ABI Land Surface Temperature (LST) algorithm generates the baseline products of land surface skin temperatures in three ABI scan modes: Full Disk, CONUS, Mesoscale.;

● Meets the GOES-R mission requirements specified for the LST product;

● Has a good heritage, will add to the LST climate data record;

● Simplicity for implementation/ease of maintenance, operational robustness, and potential for improvement.

Full Disk

CONUS

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Products

Product Accuracy Precision Range Refresh Rate Resolution

LST (CONUS) 2.5 K 2.3 K 213 ~ 330 K 60 min 2 km

LST (Full Disk) 2.5 K 2.3 K 213 ~ 333 K 60 min 10 km*

LST (Mesoscale) 2.5 K 2.3 K 213 ~ 330 K 60 min 2 km

ProductTemporal Coverage

Product Extent

Cloud Cover ConditionsProduct

Statistics

LST (CONUS) Day and Night LZA < 70Clear Conditions associated with

threshold accuracyOver specified

geographic area

LST (Full Disk) Day and Night LZA < 70Clear Conditions associated with


geographic area

LST (Mesoscale) Day and Night LZA < 70Clear Conditions associated with


geographic area

Specifications

Qualifiers

*Requirement Change Requested: to be 2 km.

Validation Strategies

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Utilize existing ground station data» Stations under GOES-R Imager coverage

» Stations under MSG/SEVIRI coverage

Ground site characterization

Stringent cloud filtering

Multiple comparisons: satellite vs satellite, satellite vs ground station.

Direct and indirect comparisons

International cooperation

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

SURFRAD Sites


Development for routine validation tools» Characterizations of SURFRAD and CRN ground sites

» Routinely acquired matchup data sets of satellite and ground Data

» Ground LST estimation

» Procedures for converting point ground LST to “pixel” ground LST

» Direct comparisons and statistics for each ground LST vs satellite LST for last x months

» Time series plots of selected coincident LST and ground LST for last x months

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Development for deep dive validation tools» All of the routine validation tools +» Data sets consisting of multiple years of clear radiances

coincident with ground LSTs» Indirect comparison and statistics for each ground LST vs

ground LST climatology for last x years» Comparisons and statistics for GOES-R LST vs other

satellite LST» Routines for calibrating LST algorithm coeffs using the

validation results

“Routine” Validation Tools

Bulk/overview analysis

Executed soon after product generation

Run routinely

Run within OSPO and STAR

Automated

“Deep Dive” Validation Tools

Detailed/point analysis

Not executed in real-time. May need to wait for other datasets

Run when more detailed analysis of product performance is needed

Run within STAR

Automated and/or Interactive components

Validation Tools

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Satellite Data

Satellite Data

Match-up Datasets Match-up Datasets

Time Match-upTime Match-up

Geolocation Match-upGeolocation Match-up

Ground Data MaskGround Data Mask

Satellite Cloud MaskSatellite Cloud Mask

Manual Cloud ControlManual Cloud Control

Ground LST Estimation/Extractio

n

Ground LST Estimation/Extractio

n

Satellite LST Calculation/Extractio

n

Satellite LST Calculation/Extractio

n

Synthetic Analysis and

Correction

Synthetic Analysis and

Correction

Ground Data Ground DataGround Data ReaderGround Data Reader

Satellite Data ReaderSatellite Data Reader

Indirect Comparison

Indirect Comparison

Direct ComparisonDirect Comparison

Statistical AnalysisStatistical Analysis

Outputs (Plots, Tables, etc.)

Outputs (Plots, Tables, etc.)

Components of Validation Tools

Routine Validation Tools-- SURFRAD data results

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Comparison results of GOES-8 LST using six SURFRAD ground station data, in 2001..

Month

Site 1 Site 2 Site 3 Site 4 Site 5 Site 6

Day Night Day Night Day Night Day Night Day Night Day Night

1 48 17 55 64 107 107 58 48 100 67 100 51

2 43 30 38 38 73 57 55 34 41 54 64 48

3 0 0 51 71 94 62 93 79 41 63 123 34

4 94 18 39 80 81 34 62 61 81 57 139 35

5 71 22 27 59 127 65 75 83 82 45 168 75

6 50 26 82 102 82 51 65 50 86 64 187 60

7 6 4 91 77 40 8 27 38 40 43 173 74

8 43 30 129 113 41 38 82 137 56 45 107 52

9 115 57 95 108 124 49 79 110 116 85 189 98

10 103 38 62 95 184 39 90 79 74 58 115 61

11 114 34 43 129 146 70 64 53 116 53 131 88

12 40 38 67 66 124 71 73 70 107 74 113 56

Total 727 314 779 1002 1223 651 823 842 940 708 1609 732

Numbers (Table, left) and scatter plots (right) of the match-up LSTs derived from GOES-8 Imager data vs. LSTs estimated from SURFRAD stations in year 2001. Data sets in plots are stratified for daytime (red) and night time (blue) atmospheric conditions

Routine Validation Tools -- A visualization interface

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T(x,y,t)

T(x0,y0,t0)

ASTER pixel The site pixelMODIS pixel

Quantitatively characterize the sub-pixel heterogeneity and evaluate whether a ground site is adequately representative for the satellite pixel. The sub-pixels may be generated from pixels of a higher-resolution satellite.

For pixel that is relatively homogeneous, analyze statistical relationship of the ground-site sub-pixel with the surrounding sub-pixels:

{T(x,y) } ~ T(x0,y0) Establish relationship between the objective pixel and its sub-pixels (i.e.,

up-scaling model), e.g., Tpixel = T(x,y) + T (time dependent?)

Site characterization analysis using ASTER data— an integrated approach for understanding site representativeness and for site-to-pixel model development

Surface heterogeneity is shown in a 4km x 4km Google map (1km x 1km, in the center box) around the Bondville station area

The Synthetic pixel/sub-pixel model

SiteMODIS Pixel-

SURFRADSynthetic Pixel-

SURFRADNearest Aster Pixel

– Synthetic pixelMean StdDev Mean StdDev Mean StdDev

Desert Rock, NV -0.44 1.84 2.09 1.69 0.04 0.44 Boulder,CO -0.49 2.08 1.49 2.90 -0.09 0.67 Fort Peck, MT 0.35 2.52 0.78 1.95 0.38 1.15 Bondville, IL -0.17 1.51 1.04 1.48 0.03 0.90 Penn State, PA -1.53 1.91 0.61 1.96 0.04 1.07

Site-to-Pixel Statistical Relationship for 5 SURFRAD sites

”Deep-Dive” Validation Tools

Others ?

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”Deep-Dive” Validation Tools Comparison of SEVIRI-Retrieved LST and station LST

at Evora

• Apparent diurnal patterns are shown in the 10 days’ LST comparison profiles for selected months.

• Comparison of LST diurnal profiles revealed higher station LST than SEVIRI LST around mid-days (i.e. maximum daily LST) and slightly higher SEVIRI LST than station LST at night (with low LST).

• The diurnal differences are larger in warm months.

We need to understand and fix this problem

EOGC 2009, May 25-29, 200912

Goodwin Creek, MS, observation pairs are about 510. View Zenith of GOES-8/-10: 42.68 0/61.890

”Deep-Dive” Validation Tools-- Directional effect study

Due to the satellite LST directional properties (surface components, topography, shadowing etc.), the satellite LST can be significantly different from different view angles. Deep dive validation tools may be used for case studies and improved algorithms.

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Complementary Posters Validation of Land Surface Temperature Algorithm for U.S. GOES-R MissionY.Yu, H. Xu, X-L. Wang, D. Tarpley, R. Hale

Development of a Multi-Satellite Validation System for NOAA/GOES-R ABI Land Surface ProductsK. Gallo, G. Stensaas,G. Chander, Y. Yu, M. Goldberg,R. Hale, and D. Tarpley

Impacts of Emissivities in the Retrieval of SEVIRI LST and the Calculation of LST from Surface Measurements H. Xu and Y. Yu

Developing Tools for LST Validation and Deep-Dive AnalysisX-L. Wang, Y. Yu, H. Xu, D. Tarpley, R. Hale

Land Surface and Air Temperature (LST & SAT) at Clear and Overcast skies K. Y. Vinnikov, Y. Yu, M. D. Goldberg, D. Tarpley, M. Chen, C. N. Long

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Fire Detection and

Characterization Application Team

Christopher Schmidt (CIMSS)Ivan Csiszar (STAR)

Wilfrid Schroeder (CICS/UMD)

Products

Fire detection and characterization algorithm properties:Refresh rate: 5 minute CONUS, 15 minute full diskResolution: 2 kmCoverage: CONUS, full diskABI version of the current GOES Wildfire Automated Biomass Burning Algorithm (WF_ABBA)Product outputs:

» Fire location

» Fire instantaneous size, temperature, and radiative power

» Metadata mask including information about opaque clouds, solar reflection block-out zones, unusable ecosystem types.

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Products

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FDCA Routine Validation

Current practice for GOES WF_ABBA:

No automated realtime method is available. Ground-based fire reports are incomplete and typically not available in realtime. At the Hazard Mapping System Human operators look at fire detections from various satellites and at satellite imagery to remove potential false alarms. This method is labor intensive and actual fire pixels are often removed.


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FDCA Routine ValidationABI near realtime validation:•Co-locate ABI fire pixels with other satellite data

• Ground-based datasets tend to be incomplete and not available in realtime• Fire detections from other satellites (polar orbiting) can be used in near

realtime• Perfect agreement is not expected. Due to resolution, viewing angle, and

sensor property differences a substantial number of valid fires will be seen by only one platform

•Other fire properties (instantaneous fire size, temperature, and radiative power) have no available near realtime validation source (see Deep-Dive tools)•Important note: the product requirement does not align with user expectations. The requirement states:

“2.0 K brightness temperature within dynamic range (275 K to 400 K)”

This applies to a pixel brightness temperature, and the algorithm achieves it for 100% of the fires where fire characteristics are calculated. When used to recalculate the input brightness temperature the fire characteristics match the input data to better than 0.0001 K.


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FDCA Validation ToolsRoutine validation tools:•Perform co-locations for individual fires and for clusters of fires•Provide statistics on matches•Table on following slide shows example of routine statistics from model- generated proxy data cases. 75 MW of fire radiative power is the estimated threshold for fire detectability.

Deep-Dive validation tools:•Allow for validation of fire location and properties•Utilize high-resolution data from satellite or aircraft to provide fire locations and enable estimates of fire size, temperature, and radiative power•Can be partially automated, availability of high resolution data is limiting factor


2020

CIRA Model Simulated Case Studies^

CIRA Truth ABI WF_ABBA

Total # of fire clusters*

Total # of ABI

fire pixels*

Total # of ABI fire

pixels > FRP of 75 MW*

Total # of

detected clusters

% Fire clusters

detected*

Total # of fire pixels detected > FRP of 75 MW*

% Fire pixels detected > FRP

of 75 MW*

% False postives(compared to model truth,

will not be available for routine validation)

Kansas CFNOCLD

9720 63288 52234 9648 99.3% 47482 90.9% <1%

Kansas VFNOCLD

5723 36919 26600 5695 99.5% 551 80.6% <1%

Kansas CFCLD

9140 56553 46446 8768 95.9% 39380 84.8% <1%

Cent. Amer. VFCLD

849 2859 1669 808 95.2% 1424 85.3% <1%

Oct 23, 2007 California VFCLD

990 4710 2388 989 99.9% 2090 87.5% <1%

Oct, 26 2007 California VFCLD

120 522 252 120 100% 211 83.7% <1%

CFNOCLD Constant Fire No Cloud^ Limit to ~ 400K minimum fire temperature

VFNOCLD Variable Fire No Cloud

CFCLD Constant Fire with Cloud* In clear sky regions, eliminating block-out zones

VFCLD Variable Fire with Cloud

Deep-dive fire detection and characterization validation tool builds on methods originally developed for MODIS and GOES Imager» Use of near-coincident (<15min) Landsat-class and airborne data to

generate sub-pixel summary statistics of fire activity– Landsat-class data are used to assess fire detection performance

History of successful applications using ASTER, Landsat TM and ETM+ to estimate MODIS and GOES fire detection probabilities and commission error rates (false alarms). Methods published in seven peer reviewed journal articles

Limited fire characterization assessment (approximate fire size only). Frequent pixel saturation and lack of middle infrared band prevent assessment of ABI’s fire characterization parameters

– Airborne sensors are used to assess fire characterization accuracy High quality middle-infrared bands provide fine resolution data (<10m) with

minimum saturation allowing full assessment of ABI’s fire characterization parameters (size, temperature, Fire Radiative Power)

Sampling is limited compared to Landsat-class data » Regional × hemispheric/global coverage» Targeting case-study analyses

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Several national and international assets will be used to support ABI fire validation» USGS Landsat Data Continuity Mission (2013)» ESA Sentinel-2 (2013)» DLR BIROS (2013)» NASA HysPIRI (TBD ~2020)» Airborne platforms (NASA/Ames Autonomous Modular Sensor-Wildfire;

USFS FireMapper) Will perform continuous assimilation, processing and archival of

reference fire data sets» Daily alerts targeting false alarms, omission of large fires

– Main output: Quick looks (PNG) for visual inspection of problem areas showing ABI pixels overlaid on high resolution reference imagery

» Probability of detection curves and commission error rates derived from several weeks/months of accumulated validation data

– Main output: Tabular (ASCII) data containing pixel-based validation summary (graphic output optional)

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Sample visual output of simulated ABI fire product (grid 2km ABI pixel footprints) overlaid on ASTER 30m resolution RGB (bands 8-3-1). Red grid cells indicate ABI fire detection pixels; green on background image corresponds to vegetation; bright red is indicative of surface fire

ASTER binary (fire – no fire) active fire mask indicating 494 (30m resolution) active fire pixels coincident with GOES-R ABI simulated fire product

Using Landsat-class imagery to validate ABI fire detection data

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For more visit:

Deep-Dive Validation of GOES-R Active Fire Detection and Characterization Product

at the poster session

land team chair: yunyue (bob) yu noaa/nesdis/star

Documents

ground lst vs satellite

point ground lst

r lst vs

lst producthas

lst mesoscale2

satellite vs ground

calibrating lst algorithm

surfrad ground station