MODIS Imagery and MODIS Imagery and Products in an Operational Products in an Operational Forecasting EnvironmentForecasting Environment

Jordan Joel GerthJordan Joel GerthCooperative Institute forCooperative Institute for

Meteorological Satellite StudiesMeteorological Satellite Studiesandand

Space Science and Engineering CenterSpace Science and Engineering CenterUniversity of Wisconsin at MadisonUniversity of Wisconsin at Madison

Wednesday, November 1, 2006Wednesday, November 1, 2006

OverviewOverview

Participating OfficesParticipating Offices AWIPS D-2DAWIPS D-2D Types of Imagery and ProductsTypes of Imagery and Products Processing and Delivery MechanismProcessing and Delivery Mechanism HurdlesHurdles Most and Least UsedMost and Least Used Strengths and WeaknessesStrengths and Weaknesses Value to ForecasterValue to Forecaster

OverviewOverview There is an ongoing two-pronged effort in There is an ongoing two-pronged effort in

support of providing MODIS data to the support of providing MODIS data to the National Weather Service:National Weather Service:• MSFC SPORT project - Short-term Prediction MSFC SPORT project - Short-term Prediction

Research and Transition Center whose goal is Research and Transition Center whose goal is to use NASA Earth Science Enterprise (ESE) to use NASA Earth Science Enterprise (ESE) observations to improve short term (0-24hr) observations to improve short term (0-24hr) forecasts. They are using University of forecasts. They are using University of Wisconsin-Madison DB MODIS and AMSR-E Wisconsin-Madison DB MODIS and AMSR-E products for distribution to forecast offices in products for distribution to forecast offices in the Southern Region.the Southern Region.

• UW-Madison - Supporting NWS MODIS Direct UW-Madison - Supporting NWS MODIS Direct Broadcast data delivery into AWIPS for the Broadcast data delivery into AWIPS for the Central Region forecast offices. Central Region forecast offices.

Instructions Available OnlineInstructions Available Onlinehttp://cimss.ssec.wisc.edu/~jordang/awips-modis/

Participating OfficesParticipating Offices

CurrentCurrent Davenport, Iowa (DVN)Davenport, Iowa (DVN) La Crosse, Wisconsin (ARX)La Crosse, Wisconsin (ARX) Milwaukee/Sullivan, Wisconsin (MKX)Milwaukee/Sullivan, Wisconsin (MKX) Riverton, Wyoming (RIW)Riverton, Wyoming (RIW)

FutureFuture Des Moines, Iowa (DMX)Des Moines, Iowa (DMX) MoreMore

AWIPS D-2DAWIPS D-2D

Advanced Weather Information Advanced Weather Information Processing SystemProcessing System

Display Two-DimensionsDisplay Two-Dimensions GUI; no command lineGUI; no command line One-stop mechanism for gathering One-stop mechanism for gathering

and viewing all operational weather and viewing all operational weather data at National Weather Service data at National Weather Service field offices, including model data, field offices, including model data, satellite data, observations, satellite data, observations, lightning, local radar, etc.lightning, local radar, etc.

AWIPS D-2DAWIPS D-2D

Panes

Types of Imagery and ProductsTypes of Imagery and Products

1 Kilometer Resolution1 Kilometer Resolution• Visible (Band 1)Visible (Band 1)• Snow/Ice (Band 7)Snow/Ice (Band 7)• Cirrus (Band 26)Cirrus (Band 26)• 3.7µm (Band 20)3.7µm (Band 20)• Water Vapor (Band 27)Water Vapor (Band 27)• IR Window (Band 31)IR Window (Band 31)• 11µm – 3.7µm product11µm – 3.7µm product

Sample ImagesSample Images

3.7µµm Cirrus

Snow Visible

Sample ImagesSample Images

WV

11µm – 3.7µmµm – 3.7µm

IR

Types of Imagery and ProductsTypes of Imagery and Products

4 Kilometer Resolution4 Kilometer Resolution• Total Precipitable Water (TPW)Total Precipitable Water (TPW)• Cloud Phase (CTP)Cloud Phase (CTP)• Cloud Top Temperature (CTT)Cloud Top Temperature (CTT)

Marine (1 Kilometer)Marine (1 Kilometer)• Sea Surface Temperature (SST)Sea Surface Temperature (SST)

Two sets: eastern and western United Two sets: eastern and western United StatesStates

Sample ImagesSample Images

TPW

SST(Daytime)

CTT

Processing MechanismProcessing Mechanism

Obtain a McIDAS (University of Obtain a McIDAS (University of Wisconsin Visualization Tool) area file Wisconsin Visualization Tool) area file of image or productof image or product

Fit to a predefined region used in Fit to a predefined region used in AWIPS (eastConus, westConus)AWIPS (eastConus, westConus)

Zero-fill area of NetCDF where there Zero-fill area of NetCDF where there is no subset of the MODIS passis no subset of the MODIS pass

Compress using zlibCompress using zlib Apply naming conventionApply naming convention

Processing MechanismProcessing Mechanism

SSEC_AWIPS_MODIS_EAST_4KM_CPI_AQUA_ YYYYMMDD_HHMM.7361

Delivery ProcessDelivery Process

LDMSpace Science andEngineering Center

Madison, WI

LDMCentral RegionHeadquarters

Kansas City, MO

LDM on LDADNWSFO

Milwaukee, WI

LDM on LDADNWSFO

La Crosse, WI

LDM on LDADNWSFO

Riverton, WY

LDM on LDADNWSFO

Davenport, IA

EXP feed

Quality control machine:Local AWIPS workstation

96 kbpsconnection

HurdlesHurdles

Local Data Manager (LDM)Local Data Manager (LDM)• Compatibility between LDM5 and LDM6Compatibility between LDM5 and LDM6• Size of queueSize of queue

Local Data and Dissemination (LDAD)Local Data and Dissemination (LDAD)• Receiving machine at NWS field offices Receiving machine at NWS field offices

is not Linux; slowis not Linux; slow BandwidthBandwidth Load timeLoad time

• LoopsLoops

LDAD

SBN/NOAAPORT

WeaknessesWeaknesses

DelayedDelayed• Processing and delivery takes over an hourProcessing and delivery takes over an hour

Working to improveWorking to improve Lack of ConsistencyLack of Consistency

• Forecasters have difficulty memorizing Terra Forecasters have difficulty memorizing Terra and Aqua pass schedulesand Aqua pass schedules

Similarity to other satellitesSimilarity to other satellites• Since GOES visible imagery is available in a Since GOES visible imagery is available in a

timely manner, there is not much benefit to timely manner, there is not much benefit to using MODIS visibleusing MODIS visible

• Addition of POES in upcoming buildsAddition of POES in upcoming builds

MODIS Imagery UsageMODIS Imagery Usage

During forecast During forecast preparation:preparation:

Most UsedMost Used• 11µm – 3.7µm 11µm – 3.7µm

Product (Fog)Product (Fog)• Total Precipitable Total Precipitable

Water (TPW)Water (TPW)• Sea Surface Sea Surface

Temperature (SST)Temperature (SST)• Water Vapor (WV)Water Vapor (WV)

Least UsedLeast Used• VisibleVisible• CirrusCirrus

Growing UseGrowing Use• Snow/IceSnow/Ice• Cloud PhaseCloud Phase

MODIS defines this

band of clouds better than GOES.

Should I addshowers for

this afternoon?

StrengthsStrengths

Creates viable connection between Creates viable connection between research environment and National research environment and National Weather Service field officesWeather Service field offices

High resolution, better qualityHigh resolution, better quality• Depiction of small-scale featuresDepiction of small-scale features

New productsNew products• Cloud PhaseCloud Phase• Sea Surface TemperatureSea Surface Temperature

UpwellingUpwelling

Value to ForecasterValue to Forecaster

Near-term (less than 12 hours) forecastsNear-term (less than 12 hours) forecasts• Diagnosing heavy precipitation potentialDiagnosing heavy precipitation potential

Total Precipitable Water (TPW)Total Precipitable Water (TPW)

• Determining precipitation typeDetermining precipitation type Snow or freezing drizzle?Snow or freezing drizzle?

Short-term (12 to 36 hours) forecastsShort-term (12 to 36 hours) forecasts• Areas of fog formationAreas of fog formation• Temperatures in lakeshore areasTemperatures in lakeshore areas

Post-event analysisPost-event analysis• Temperature of significantTemperature of significant

convective cellsconvective cells

Value to ForecasterValue to Forecaster

AviationAviation• Small-scale orographic turbulenceSmall-scale orographic turbulence

ClimatologyClimatology• Diagnosing areas of accumulated snowDiagnosing areas of accumulated snow• Formation of ice on sizeable lakes and Formation of ice on sizeable lakes and

other waterwaysother waterways MarineMarine

• Wind shift on Great LakesWind shift on Great Lakes Local phenomenaLocal phenomena

MAIN SHORT TERM FORECAST PROBLEM IS EAST FLOW AND MARINE LAYER INFLUENCE OVER EASTERN WISCONSIN...AND DENSE FOG POTENTIAL IN THE WEST. THINK MOST OF THE DENSE FOG WOULD BE IN THE RIVER VALLEYS...WITH A TENDENCY FOR PATCHY FOG AND SOME STRATUS AGAIN IN THE EAST WITH MORE OF A GRADIENT. MODIS 1 KM IMAGERY MODIS 1 KM IMAGERY LAST NIGHT SHOWED THE DENSE FOG IN LONE ROCK AND BOSCOBEL WAS CONFINED TO LAST NIGHT SHOWED THE DENSE FOG IN LONE ROCK AND BOSCOBEL WAS CONFINED TO THE IMMEDIATE WISCONSIN RIVER VALLEY...IMPORTANT INFORMATION. THE LOCAL THE IMMEDIATE WISCONSIN RIVER VALLEY...IMPORTANT INFORMATION. THE LOCAL RIVER VALLEY DENSE FOG IS NOT SEEN IN THE NORMAL 2 KM GOES. (HENTZ/MKX)RIVER VALLEY DENSE FOG IS NOT SEEN IN THE NORMAL 2 KM GOES. (HENTZ/MKX)

MAIN SHORT TERM FORECAST PROBLEM IS EAST FLOW AND MARINE LAYER INFLUENCE OVER EASTERN WISCONSIN...AND DENSE FOG POTENTIAL IN THE WEST. THINK MOST OF THE DENSE FOG WOULD BE IN THE RIVER VALLEYS...WITH A TENDENCY FOR PATCHY FOG AND SOME STRATUS AGAIN IN THE EAST WITH MORE OF A GRADIENT. MODIS 1 KM IMAGERY MODIS 1 KM IMAGERY LAST NIGHT SHOWED THE DENSE FOG IN LONE ROCK AND BOSCOBEL WAS CONFINED TO LAST NIGHT SHOWED THE DENSE FOG IN LONE ROCK AND BOSCOBEL WAS CONFINED TO THE IMMEDIATE WISCONSIN RIVER VALLEY...IMPORTANT INFORMATION. THE LOCAL THE IMMEDIATE WISCONSIN RIVER VALLEY...IMPORTANT INFORMATION. THE LOCAL RIVER VALLEY DENSE FOG IS NOT SEEN IN THE NORMAL 2 KM GOES. (HENTZ/MKX)RIVER VALLEY DENSE FOG IS NOT SEEN IN THE NORMAL 2 KM GOES. (HENTZ/MKX)

Area Forecast DiscussionArea Forecast Discussion

Interesting ExamplesInteresting Examples

Courtesy ofCourtesy of

Scott BachmeierScott Bachmeier

(CIMSS/SSEC)(CIMSS/SSEC)

Future DevelopmentsFuture Developments

Guided by needs of the forecastersGuided by needs of the forecasters• Constrained by bandwidthConstrained by bandwidth

True color imageryTrue color imagery• Fixed enhancement of 256 colorsFixed enhancement of 256 colors

250 m visible imagery250 m visible imagery• Weigh operational significance against Weigh operational significance against

interesting aspects and size (bandwidth interesting aspects and size (bandwidth usage) of the productusage) of the product

Normalized Difference Vegetation Normalized Difference Vegetation Index (NDVI)Index (NDVI)

ConclusionConclusion

With the duties of With the duties of the forecaster in the forecaster in mind, the MODIS in mind, the MODIS in AWIPS project can AWIPS project can be successfulbe successful• ““How can MODIS How can MODIS

imagery enhance imagery enhance the forecasting the forecasting process?”process?”

Questions?Questions?

Jordan Joel Gerth, CIMSS/SSEC, October 2006