Remote Sensing in Hydrology Robert J. Kuligowski, Ph. D. NOAA/NESDIS Office of Research and...

Remote Sensing Remote Sensing in Hydrologyin Hydrology

Robert J. Kuligowski, Ph. D.Robert J. Kuligowski, Ph. D.NOAA/NESDIS Office of Research and ApplicationsNOAA/NESDIS Office of Research and Applications

[email protected]@noaa.gov

Presentation to NWS/WMO Hydrologic Forecasting CoursePresentation to NWS/WMO Hydrologic Forecasting CourseKansas City, MOKansas City, MO29 October 200329 October 2003

OutlineOutline

PrecipitationPrecipitation Theory/basis—visible/IR and microwaveTheory/basis—visible/IR and microwave Specific Algorithms and ExamplesSpecific Algorithms and Examples Forecasting ToolsForecasting Tools Validation EffortsValidation Efforts

Surface ConditionsSurface Conditions VegetationVegetation SnowSnow Surface WetnessSurface Wetness Flood InundationFlood Inundation

Part I: PrecipitationPart I: Precipitation

Theory/basis—visible/IR and Theory/basis—visible/IR and microwavemicrowave

Specific Algorithms and ExamplesSpecific Algorithms and Examples Forecasting ToolsForecasting Tools Validation EffortsValidation Efforts

Precipitation—Theory/Precipitation—Theory/Basis Basis

Visible (VIS)/Infrared (IR) AlgorithmsVisible (VIS)/Infrared (IR) Algorithms Basic assumption is that cloud-top temperature Basic assumption is that cloud-top temperature

brightness is related to cloud height, which in brightness is related to cloud height, which in turn is related to cloud thickness, and to turn is related to cloud thickness, and to rainfall raterainfall rate

Colder, brighter clouds are associated with heavier Colder, brighter clouds are associated with heavier rainrain

Warmer, less bright clouds are associated with light Warmer, less bright clouds are associated with light or no rainor no rain

Reasonable assumption for convective cloudsReasonable assumption for convective clouds Poor assumption forPoor assumption for

stratiform clouds (warm, but wet)stratiform clouds (warm, but wet) cirrus clouds (cold, but rain-free)cirrus clouds (cold, but rain-free)

Illustration of the IR signal from different cloud types

CirrusTb=210 K

NimbostratusTb=240 K

200 250 290T (K)

CumulonimbusTb=200 K


Microwave (MW) AlgorithmsMicrowave (MW) Algorithms ScatteringScattering: ice in clouds scatters : ice in clouds scatters

(warm) terrestrial radiation downward, (warm) terrestrial radiation downward, producing cold areas in imagery.producing cold areas in imagery.

Rainfall rates are related to the magnitude Rainfall rates are related to the magnitude of the resulting brightness temperature of the resulting brightness temperature depression.depression.

StrengthStrength: can be applied to high-: can be applied to high-frequency channels where surface effects frequency channels where surface effects are not detected: works over both land and are not detected: works over both land and oceanocean

WeaknessWeakness: poor at detecting precipitation : poor at detecting precipitation clouds with little or no ice (e.g. warm clouds with little or no ice (e.g. warm orographic clouds in the tropics)orographic clouds in the tropics)


Microwave (MW) AlgorithmsMicrowave (MW) Algorithms EmissionEmission: water in clouds emits : water in clouds emits

radiation, can be seen against a radiation, can be seen against a radiatively cold background (i.e. radiatively cold background (i.e. oceans).oceans).

Rainfall rates are related to the magnitude Rainfall rates are related to the magnitude of the resulting brightness temperature of the resulting brightness temperature differencedifference

StrengthStrength: Sensitive to clouds with little : Sensitive to clouds with little or no iceor no ice

WeaknessWeakness: must know terrestrial : must know terrestrial radiances without cloud beforehand; radiances without cloud beforehand; generally applicable over oceans but not generally applicable over oceans but not landland

Land (Scattering) Ocean (Emission)

Lower Tb

above cloud

Higher Tb

above clear air

Lower Tb

above clear air

Higher Tb

above cloud

Low ε

High ε

Precipitation—IR/VIS Precipitation—IR/VIS vs. MW vs. MW

Physical Robustness:Physical Robustness: Microwave radiances are sensitive to moisture Microwave radiances are sensitive to moisture

throughout the cloudthroughout the cloud IR/VIS data reflect cloud-top conditions only and IR/VIS data reflect cloud-top conditions only and

thus are more weakly related to actual rainfall rates thus are more weakly related to actual rainfall rates over a wider range of conditions than MW over a wider range of conditions than MW radiances.radiances.

Space/Time ResolutionSpace/Time Resolution IR/VIS data are available at 4 km/1km resolution IR/VIS data are available at 4 km/1km resolution

(GOES) on geostationary platforms, allowing looks (GOES) on geostationary platforms, allowing looks in many locations every 15 minutes—suitable for in many locations every 15 minutes—suitable for extreme precipitation events at short time scalesextreme precipitation events at short time scales

MW instruments are presently restricted to polar-MW instruments are presently restricted to polar-orbiting platforms, limiting views to 2 per day per orbiting platforms, limiting views to 2 per day per satellite—more suitable for larger scales in time satellite—more suitable for larger scales in time and spaceand space



Specific Algorithms and ExamplesSpecific Algorithms and Examples IR AlgorithmsIR Algorithms Microwave AlgorithmsMicrowave Algorithms Blended AlgorithmsBlended Algorithms

Forecasting ToolsForecasting Tools Validation EffortsValidation Efforts

IR Algorithms: Hydro-IR Algorithms: Hydro-Estimator (H-E)Estimator (H-E)

The operational satellite rainfall algorithm The operational satellite rainfall algorithm at NESDIS since August 2002at NESDIS since August 2002

Uses 10.7-Uses 10.7-μμm (Tm (T10.710.7) brightness temperature ) brightness temperature to determine raining areas and rain ratesto determine raining areas and rain rates Assigns rain only to regions where TAssigns rain only to regions where T10.710.7 is below is below

local average; i.e. active precipitating coreslocal average; i.e. active precipitating cores Rain rates are a function of both TRain rates are a function of both T10.710.7 and its and its

value relative to the local average—further value relative to the local average—further enhancement of rain rates in precipitating coresenhancement of rain rates in precipitating cores

H-E ContinuedH-E Continued

Sample Hydro-Estimator Rain Rate Curves as a Function of Precipitable Water

H-E ContinuedH-E Continued

Adjustments using Numerical Weather Adjustments using Numerical Weather Prediction Model Data, including:Prediction Model Data, including: Relative humidity to reduce precipitation Relative humidity to reduce precipitation

in arid regions with significant sub-cloud in arid regions with significant sub-cloud evaporation of raindropsevaporation of raindrops

Wind fields interfaced with digital Wind fields interfaced with digital topography to determine orographic topography to determine orographic enhancements or reductions of enhancements or reductions of precipitationprecipitation

Also adjustments for parallax in Also adjustments for parallax in regions away from satellite sub-pointregions away from satellite sub-point

H-E AvailabilityH-E Availability

Produced every 15 minutes over the Produced every 15 minutes over the continental United States using GOES-10 and continental United States using GOES-10 and -12 data-12 data

Produced in other regions throughout the Produced in other regions throughout the globe whenever IR imagery are availableglobe whenever IR imagery are available

Instantaneous rates are summed into 1-, 3-, 6- Instantaneous rates are summed into 1-, 3-, 6- and 24-hourly totalsand 24-hourly totals

Available in real-time for entire Western Available in real-time for entire Western Hemisphere over the Internet from the Hemisphere over the Internet from the NESDIS Flash Flood Home pageNESDIS Flash Flood Home page

Contact: Rod Scofield at Contact: Rod Scofield at [email protected]@noaa.gov

H-E—Where to Get DataH-E—Where to Get Datahttp://orbit-net.nesdis.noaa.gov/arad/http://orbit-net.nesdis.noaa.gov/arad/

ht/ffht/ff

Continental U.S.

Central & South America.

H-E—ExampleH-E—Example

24-h Total Ending 1200 UTC 7 October 2003

T.D. Nora

T.D. Olaf

IR Algorithms: GMSRAIR Algorithms: GMSRA GMSRA=GMSRA=GGOES OES MMulti-ulti-SSpectral pectral RRainfall ainfall

AAlgorithmlgorithm Uses Data from 4 Different Channels:Uses Data from 4 Different Channels:

Visible (0.69 Visible (0.69 μμm)—discriminate between thin m)—discriminate between thin (nonraining) cirrus and thicker (raining) clouds(nonraining) cirrus and thicker (raining) clouds

““Short” IR Window (3.9-Short” IR Window (3.9-μμm)—use reflectivity to m)—use reflectivity to identify clouds that are warm but have large identify clouds that are warm but have large particles near cloud-top and are thus producing rainparticles near cloud-top and are thus producing rain

Water Vapor (6.7-Water Vapor (6.7-μμm)—warm signature above m)—warm signature above overshooting cloud tops differentiates from cirrusovershooting cloud tops differentiates from cirrus

IR Window (10.7-IR Window (10.7-μμm)—texture screening of cirrus m)—texture screening of cirrus clouds (low texture=cirrus; high texture=rain) and clouds (low texture=cirrus; high texture=rain) and calculation of rainfall rate (but dependent only on calculation of rainfall rate (but dependent only on value at pixel of interest)value at pixel of interest)

GMSRA ContinuedGMSRA Continued

Adjustment for rainfall evaporation using Adjustment for rainfall evaporation using precipitable water and relative humidity data precipitable water and relative humidity data from numerical weather modelsfrom numerical weather models

Produced every 15 minutes for the continental Produced every 15 minutes for the continental United States using GOES-10 and -12 dataUnited States using GOES-10 and -12 data

Instantaneous rates are summed into 1-, 3-, 6- Instantaneous rates are summed into 1-, 3-, 6- and 24-hourly totalsand 24-hourly totals

Available in real-time over the Internet from Available in real-time over the Internet from the NESDIS Flash Flood Home pagethe NESDIS Flash Flood Home page

Contact: Mamoudou Ba at Contact: Mamoudou Ba at [email protected]@atmos.umd.edu

GMSRA—Where to Get GMSRA—Where to Get DataData

http://orbit-net.nesdis.noaa.gov/arad/http://orbit-net.nesdis.noaa.gov/arad/ht/ffht/ff

GMSRA—ExampleGMSRA—Example

24-h Total Ending 1200 UTC 6 October 2003

MW Algorithms: SSM/IMW Algorithms: SSM/I

SSM/I=SSM/I=SSpecial pecial SSensor ensor MMicrowave/icrowave/IImagermager Seven channels: 19, 22, 37, and 85 GHz with Seven channels: 19, 22, 37, and 85 GHz with

both horizontal (H) and vertical (V) polarization both horizontal (H) and vertical (V) polarization (except no H on 22 GHz)(except no H on 22 GHz)

Two separate algorithms:Two separate algorithms: EmissionEmission: T: TBB at 19V, 22V used to create “clear- at 19V, 22V used to create “clear-

sky” 37V values—degree of warming of observed sky” 37V values—degree of warming of observed 37V related to rainfall rate 37V related to rainfall rate over waterover water in regions in regions of of weak scatteringweak scattering

Scattering:Scattering: T TBB at 19V, 22V used to estimate at 19V, 22V used to estimate “clear-sky” 85V values—degree of cooling of “clear-sky” 85V values—degree of cooling of observed 85V related to rainfall rate (separate observed 85V related to rainfall rate (separate calibrations for land and ocean)calibrations for land and ocean)

SSM/I ContinuedSSM/I Continued

Approximate horizontal resolution of Approximate horizontal resolution of 25 km25 km

Maximum rainfall rate of 35 mm/hMaximum rainfall rate of 35 mm/h Available 6 times per day (Available 6 times per day (~0600, ~0600,

0915, 1100, 1800, 2115, 2300 Local 0915, 1100, 1800, 2115, 2300 Local Standard Time); Standard Time); estimates produced estimates produced globally as data become availableglobally as data become available

Contact: Ralph Ferraro at Contact: Ralph Ferraro at [email protected]@noaa.gov

SSM/I—Where to Get SSM/I—Where to Get DataData

http://www.osdpd.noaa.gov/PSB/SHARED_PROCESSING/SHARED_PROCESSING.htmlhttp://www.osdpd.noaa.gov/PSB/SHARED_PROCESSING/SHARED_PROCESSING.html

SSM/I—ExampleSSM/I—Example

Rain Rate During the 4 h Ending 1600 UTC 8 October 2003

MW Algorithms—AMSU-MW Algorithms—AMSU-BB

AMSU=AMSU=AAdvanced dvanced MMicrowave icrowave SSounding ounding UUnitnit

Single Single scatteringscattering algorithm over both land algorithm over both land and ocean: difference between and ocean: difference between brightness brightness temperatures at 89 and 150 GHz is related temperatures at 89 and 150 GHz is related to rainfall rateto rainfall rate

Maximum rain rate of 35 mm/hMaximum rain rate of 35 mm/h Approximately 16-km horizontal resolutionApproximately 16-km horizontal resolution Available 6 times per day (~0130, 0730, Available 6 times per day (~0130, 0730,

1030, 1330, 1930, and 2230 Local Standard 1030, 1330, 1930, and 2230 Local Standard Time); estimates produced globally as data Time); estimates produced globally as data become availablebecome available

Contact: Ralph Ferraro at Contact: Ralph Ferraro at [email protected]@noaa.gov

AMSU-B—Where to Get AMSU-B—Where to Get DataData

http://orbit-net.nesdis.noaa.gov/arad2/MSPPS/http://orbit-net.nesdis.noaa.gov/arad2/MSPPS/index.htmlindex.html

AMSU-B—ExampleAMSU-B—Example

NOAA-16 Rain Rate at 1330 LST 7 October 2003

IR-Microwave AlgorithmsIR-Microwave Algorithms

Numerous algorithms for combining the Numerous algorithms for combining the relative accuracy of MW rainfall rates relative accuracy of MW rainfall rates with the space/ time resolution of IR datawith the space/ time resolution of IR data

Most algorithms calibrate a relationship Most algorithms calibrate a relationship between MW rainfall rates and 10.7between MW rainfall rates and 10.7μμm m IR brightness temperaturesIR brightness temperatures

Three types (Turk algorithm, PERSIANN, Three types (Turk algorithm, PERSIANN, and CMORPH) running in real-time at and CMORPH) running in real-time at NOAANOAA

IR-MW Algorithms: TurkIR-MW Algorithms: Turk Developed by F. J. Turk of Naval Research Developed by F. J. Turk of Naval Research

Laboratory (NRL)Laboratory (NRL) Determines relationship between MW rain Determines relationship between MW rain

rate and IR brightness temperature by rate and IR brightness temperature by matching cumulative distribution functions matching cumulative distribution functions (CDF’s); e.g. 90(CDF’s); e.g. 90thth percentile rain rate is percentile rain rate is assigned to temperature corresponding to assigned to temperature corresponding to 9090thth percentile percentile

Calibration updated every few hours for a Calibration updated every few hours for a 5x5-degree region5x5-degree region

Also includes H-E adjustments based on Also includes H-E adjustments based on model data (moisture, etc.)model data (moisture, etc.)

Contact: Joe Turk at [email protected]: Joe Turk at [email protected]

Turk Blend—Where to Turk Blend—Where to Get DataGet Data

or (CONUS only): or (CONUS only): http://orbit-net.nesdis.noaa.gov/arad/ht/ff/blended.html

http://www.nrlmry.navy.mil/sat-bin/http://www.nrlmry.navy.mil/sat-bin/rain.cgirain.cgi

Turk Blend—ExampleTurk Blend—Example

24-hour Total Ending 0000 UTC 9 October 2003

IR-MW Algorithms: IR-MW Algorithms: PERSIANNPERSIANN

PPrecipitation recipitation EEstimation from stimation from RRemotely emotely SSensed ensed IInformation using nformation using AArtificial rtificial NNeural eural NNetworksetworks

Developed by Soroosh Sorooshian’s research Developed by Soroosh Sorooshian’s research group at the U. of Arizona (now at UC-Irvine)group at the U. of Arizona (now at UC-Irvine)

Uses artificial neural networks to relate 10.7-Uses artificial neural networks to relate 10.7-μμm brightness temperature (both pixel values m brightness temperature (both pixel values and local patterns) to MW rainfall ratesand local patterns) to MW rainfall rates

About to begin routine global estimates at About to begin routine global estimates at 0.25-degree resolution0.25-degree resolution

Will have link from NESDIS Flash Flood Web Will have link from NESDIS Flash Flood Web pagepage

Contact: Bob Kuligowski at Contact: Bob Kuligowski at [email protected]@noaa.gov

PERSIANN—ExamplePERSIANN—Example

6-hour Total Ending 1200 UTC 15 July 2003

(Tropical Storm Bill)

PERSIANN Radar-Raingauge Blend

IR-MW Algorithms: IR-MW Algorithms: CMORPHCMORPH

CMORPH=CMORPH=CCPC PC MORPHMORPHing techniqueing technique Developed by R. Joyce and colleagues at the Developed by R. Joyce and colleagues at the

Climate Prediction Center (CPC)Climate Prediction Center (CPC) Uses IR imagery to interpolate the movement Uses IR imagery to interpolate the movement

of rainfall areas in MW imagery in between of rainfall areas in MW imagery in between imagesimages

Also interpolates growth/decay of MW rainfall Also interpolates growth/decay of MW rainfall between MW imagesbetween MW images

Produced globally at 0.727-degree resolution Produced globally at 0.727-degree resolution in near-real timein near-real time

Contact: Bob Joyce at [email protected]: Bob Joyce at [email protected]

CMORPH—ExampleCMORPH—Example

24-h Totals ending 0000 UTC 8 October 2003

CMORPH—Where to Get CMORPH—Where to Get the Datathe Data

http://www.cpc.noaa.gov/products/janowiak/MW-http://www.cpc.noaa.gov/products/janowiak/MW-precip_index.htmlprecip_index.html



Specific Algorithms and ExamplesSpecific Algorithms and Examples Forecasting ToolsForecasting Tools

TRaPTRaP Hydro-NowcasterHydro-Nowcaster

Validation EffortsValidation Efforts

Forecasting Tools: TRaPForecasting Tools: TRaP

TRaP=TRaP=TTropical ropical RaRainfall infall PPotential—24-otential—24-hour precipitation forecasthour precipitation forecast

Produced by extrapolating microwave-Produced by extrapolating microwave-based instantaneous precipitation based instantaneous precipitation estimates along the estimates along the predictedpredicted storm storm tracktrack

Forecasts produced automatically Forecasts produced automatically whenever a new microwave image or whenever a new microwave image or track forecast becomes available—track forecast becomes available—posted in Web in graphic formatposted in Web in graphic format

TRaP—Where to Get the TRaP—Where to Get the DataData

http://www.ssd.noaa.gov/PS/TROP/trap-img.htmlhttp://www.ssd.noaa.gov/PS/TROP/trap-img.html

Pre-landfall TRaP for the 24 h ending 1200 UTC 16 July 2003

TRaP Forecast Radar-Raingauge Blend

TRaP—ExampleTRaP—Example(Hurricane Claudette)(Hurricane Claudette)

TRaP—ExampleTRaP—Example

TRaP for Tropical Cyclone Manou—1815 UTC 8 May 2003

Forecast Tools: Hydro-Forecast Tools: Hydro-NowcasterNowcaster

0-3 hour nowcasts of precipitation 0-3 hour nowcasts of precipitation amountamount

Based on extrapolation of H-E Based on extrapolation of H-E instantaneous rainfall rates along the instantaneous rainfall rates along the direction of direction of movementmovement of precipitation of precipitation cellscells

Rain rates are enhanced or reduced with Rain rates are enhanced or reduced with time based on previous trendstime based on previous trends

Produced routinely for the continental Produced routinely for the continental U.S.U.S.

Hydro-Nowcaster—Where to Hydro-Nowcaster—Where to Get DataGet Data

http://orbit-net.nesdis.noaa.gov/arad/http://orbit-net.nesdis.noaa.gov/arad/ht/ffht/ff

Hydro-Nowcaster—Hydro-Nowcaster—ExampleExample

3-h Forecast for 1345-1645 UTC 9 October 2003



Specific Algorithms and ExamplesSpecific Algorithms and Examples Forecasting ToolsForecasting Tools Validation EffortsValidation Efforts

Satellite QPE Validation: Satellite QPE Validation: NESDISNESDIS

Validation over continental U.S. against Validation over continental U.S. against raingauges (24-h amounts) and 4-km raingauges (24-h amounts) and 4-km Stage IV radar/raingauge blend (6-h Stage IV radar/raingauge blend (6-h amounts)amounts)

Statistics produced and displayed once Statistics produced and displayed once per dayper day

Daily spatial plots of all algorithms for 6 Daily spatial plots of all algorithms for 6 regions for comparisonregions for comparison

Archive of digital data also available for Archive of digital data also available for analysisanalysis

NESDIS Satellite QPE NESDIS Satellite QPE Validation Web PageValidation Web Page

http://orbit-net.nesdis.noaa.gov/arad/ht/ff/validation/validation.htmlhttp://orbit-net.nesdis.noaa.gov/arad/ht/ff/validation/validation.html

DigitalArchive

Click on mapregions forstatistics

Algorithmdescriptions

Satellite QPE Validation: Satellite QPE Validation: CPCCPC

Validation against CPC daily 0.25-Validation against CPC daily 0.25-degree raingauge analysis over the degree raingauge analysis over the continental U.S.continental U.S.

Evaluation of 14 different IR, MW, Evaluation of 14 different IR, MW, and PR+MW algorithms from and PR+MW algorithms from different agenciesdifferent agencies

Numerous statistics for comparison, Numerous statistics for comparison, plus spatial plots of all algorithmsplus spatial plots of all algorithms

CPC Validation Web PageCPC Validation Web Pagehttp://cpc.ncep.noaa.gov/products/janowiak/us_web.htmlhttp://cpc.ncep.noaa.gov/products/janowiak/us_web.html

Satellite QPE Validation: Satellite QPE Validation: AustraliaAustralia

Validation over Australia against Validation over Australia against 0.25-degree daily raingauge analysis0.25-degree daily raingauge analysis

Evaluation of 13 different IR, MW, Evaluation of 13 different IR, MW, and PR+MW algorithms plus and PR+MW algorithms plus precipitation forecasts from 4 precipitation forecasts from 4 numerical weather modelsnumerical weather models

Numerous statistics for comparison, Numerous statistics for comparison, plus spatial plots of all algorithmsplus spatial plots of all algorithms

Australian Bureau of Australian Bureau of Meteorology Validation Meteorology Validation

PagePagehttp://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.htmlhttp://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.html

Part II: Surface Part II: Surface ConditionsConditions

VegetationVegetation SnowSnow Surface WetnessSurface Wetness Flood InundationFlood Inundation

Vegetation ProductsVegetation Productshttp://www.osdpd.noaa.gov/PSB/IMAGES/gvi.htmlhttp://www.osdpd.noaa.gov/PSB/IMAGES/gvi.html

Vegetation Products: Vegetation Products: NDVINDVI

NNormalized ormalized DDifference ifference VVegetation egetation IIndexndex

Values from -0.1 to Values from -0.1 to +0.703+0.703

NDVI<0—bodies of NDVI<0—bodies of water, clouds, rain, or water, clouds, rain, or snowsnow

0<NDVI<0.1—rocks, 0<NDVI<0.1—rocks, bare soil, desertbare soil, desert

NDVI>0.1—vegetation, NDVI>0.1—vegetation, with biomass with biomass proportional to NDVI proportional to NDVI valuevalue

0.0 0.1

Vegetation Products: Vegetation Products: Fractional VegetationFractional Vegetation

NDVI converted to NDVI converted to fractional vegetation fractional vegetation using a linear using a linear relationship:relationship:

NDVI≤0.07NDVI≤0.070.0 0.0 fractionfraction

NDVI≥0.57NDVI≥0.571.0 1.0 fractionfraction

Vegetation Products: Vegetation Products: Vegetative HealthVegetative Health

Uses a combination of visible, 0.85-µm, and Uses a combination of visible, 0.85-µm, and 10.8-µm bands of the polar-orbiting Advanced 10.8-µm bands of the polar-orbiting Advanced Very High Resolution Radiometer (AVHRR)Very High Resolution Radiometer (AVHRR)

Sensitive to clorophyll and moisture content in Sensitive to clorophyll and moisture content in the vegetation and to surface temperaturethe vegetation and to surface temperature

Ranges from 0 (extremely poor) to 50 (fair) to Ranges from 0 (extremely poor) to 50 (fair) to 100 (excellent)100 (excellent)

Values below 35 indicate vegetative stress and Values below 35 indicate vegetative stress and droughtdrought

16-km spatial resolution; updated once per 16-km spatial resolution; updated once per weekweek

Contact: Felix Kogan at [email protected]: Felix Kogan at [email protected]

Vegetative Health: Where Vegetative Health: Where to Get the Datato Get the Data

http://orbit-net.nesdis.noaa.gov/crad/sat/surf/vci/index.htmlhttp://orbit-net.nesdis.noaa.gov/crad/sat/surf/vci/index.html

Vegetative Health: Vegetative Health: ExampleExample

Comparison for Africa for 05 October 2002 and 2003Comparison for Africa for 05 October 2002 and 2003


VegetationVegetation SnowSnow

AutomatedAutomated ManualManual

Surface WetnessSurface Wetness Flood InundationFlood Inundation

Automated Snow Products—Automated Snow Products—Where to Get DataWhere to Get Data


Link to current products

Link to previous day’s products

Experimental Snow Water Equivalent (SWE)

Automated Snow Cover—Automated Snow Cover—ExampleExample

NOAA-16 AMSU Snow Cover at 1330 LST 9 October 2003

Snow Water Equivalent Snow Water Equivalent (SWE)—Example(SWE)—Example

NOAA-16 AMSU SWE for 9 October 2003

Manual Snow Products—Manual Snow Products—Where to Get DataWhere to Get Data

http://www.ssd.noaa.gov/PS/SNOW/http://www.ssd.noaa.gov/PS/SNOW/

Data archive from past years

Current and recent daily snow products by region

Manual Snow Cover—Manual Snow Cover—ExampleExample

Snow Cover for 9 October 2003

Estimates are Estimates are produced daily produced daily by Satellite by Satellite Analysis Branch Analysis Branch (SAB) (SAB) forecasters forecasters using satellite using satellite imagery and imagery and surface surface observations.observations.

Surface Wetness—Where to Surface Wetness—Where to Get DataGet Data


Link to experimental surface wetness for US and China

Surface Wetness—Surface Wetness—ExampleExample

NOAA-16 AMSU Wetness Products for 9 October 2003

Surface Wetness and Precipitation

24-h Change in Surface Wetness

Flood InundationFlood Inundationhttp://www.dartmouth.edu/~floodshttp://www.dartmouth.edu/~floods

Monitors flooding Monitors flooding worldwideworldwide

Uses data from a Uses data from a combination of combination of satellites to satellites to produce produce inundation images inundation images for larger floodsfor larger floods

Archives availableArchives available

Flood Inundation—Flood Inundation—ExampleExample

Flooding in southwestern Mexico from Hurricane Marty—24 September 2003

Questions?Questions?

Remote Sensing in Hydrology Robert J. Kuligowski, Ph. D. NOAA/NESDIS Office of Research and...

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