national mosaic and quantitative precipitation estimation ...climate, water, and weather services...

Unidata seminar 3/28/05 0

National Mosaic and Quantitative National Mosaic and Quantitative Precipitation Estimation Project Precipitation Estimation Project

(NMQ)(NMQ)

Kenneth Howard, Dr. Jian Zhang, Steve Vasiloff, Kevin Kenneth Howard, Dr. Jian Zhang, Steve Vasiloff, Kevin Kelleher, and Dr. JJ GourleyKelleher, and Dr. JJ GourleyNational Severe Storms LaboratoryNational Severe Storms Laboratory

Dr. DJ SeoDr. DJ Seo and and David KitzmillerDavid KitzmillerNational Weather Service, OHDNational Weather Service, OHD

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Strategic PartnershipsStrategic Partnerships

Federal Aviation AdministrationFederal Aviation AdministrationConvective Weather PDTConvective Weather PDT

Chuck Dempsey, Jason Wilhite and Dr. Robert MaddoxChuck Dempsey, Jason Wilhite and Dr. Robert MaddoxSRP, Salt River Project, Tempe, AZ, USASRP, Salt River Project, Tempe, AZ, USA

Dr. Paul Chiou, Dr. Chia Rong Chen, and Dr. PaoDr. Paul Chiou, Dr. Chia Rong Chen, and Dr. Pao--Liang ChangLiang ChangCentral Weather Bureau, Taipei, TaiwanCentral Weather Bureau, Taipei, Taiwan

Weather Decision Technologies, Norman, Oklahoma, USAWeather Decision Technologies, Norman, Oklahoma, USA

Scientific CollaboratorsScientific CollaboratorsMike Smith, George Smith, Feng Ding, Chandra Kondragunta, Jon Mike Smith, George Smith, Feng Ding, Chandra Kondragunta, Jon Roe,Roe,

and Gary Carterand Gary CarterNWS, Office of Hydrological DevelopmentNWS, Office of Hydrological Development

Dr. Marty Ralph and Dr. Dave KingsmillDr. Marty Ralph and Dr. Dave KingsmillNOAA, Environmental Technology LaboratoryNOAA, Environmental Technology Laboratory

Andy Edman and Kevin Warner Andy Edman and Kevin Warner NWS, Western Region HeadquartersNWS, Western Region Headquarters

Arthur HenkelArthur HenkelCaliforniaCalifornia--Nevada RFCNevada RFC

Dr. Thomas Graziano and Mary MulluskyDr. Thomas Graziano and Mary MulluskyNWS Office of Climate, Water, and Weather ServicesNWS Office of Climate, Water, and Weather Services

Steve HunterSteve HunterUSGS, Bureau of ReclamationUSGS, Bureau of Reclamation

Dr. Robert KuligowskiDr. Robert KuligowskiNOAA National Environmental Satellite, Data and Information ServNOAA National Environmental Satellite, Data and Information Serviceice

Dr. Dr. Curtis MarshallCurtis MarshallNOAA National Center for Environmental PredictionNOAA National Center for Environmental Prediction

Basic Challenges of water, floods Basic Challenges of water, floods and water resource managementand water resource management

Too little too lateToo little too late (drought)(drought)

Too much too soonToo much too soon (flash flood)(flash flood)



What is NMQ?What is NMQ?The National Mosaic and QPE (NMQ) project is a joint The National Mosaic and QPE (NMQ) project is a joint initiative between NSSL, FAA, NCEP and the NWS/Office initiative between NSSL, FAA, NCEP and the NWS/Office of Hydrologic Development (OHD) and the NWS/Office of of Hydrologic Development (OHD) and the NWS/Office of Climate, Water, and Weather Services (OCWWS) to address Climate, Water, and Weather Services (OCWWS) to address (among others) the pressing need for (among others) the pressing need for –– highhigh--resolution national 3resolution national 3--D radar mosaics for D radar mosaics for

atmospheric data assimilation and severe weather atmospheric data assimilation and severe weather identification and predictionidentification and prediction

–– multi sensor QPE and short term QPF for all seasons, multi sensor QPE and short term QPF for all seasons, regions, and terrains in support of operational regions, and terrains in support of operational hydrometeorological products and distributed hydrologic hydrometeorological products and distributed hydrologic modelingmodeling

–– Research to operations infusion pathwayResearch to operations infusion pathway

Relevance of NMQ?Relevance of NMQ?

Monitoring and prediction of water underpins the nation’s Monitoring and prediction of water underpins the nation’s health, economy, security, and ecology.health, economy, security, and ecology.However there exists no seamless high resolution systematic However there exists no seamless high resolution systematic monitoring of fresh water resources in North Americamonitoring of fresh water resources in North AmericaThe scientific and political challenges are significant The scientific and political challenges are significant requiring a community based and multirequiring a community based and multi--faceted approach for faceted approach for fresh water monitoring and prediction.fresh water monitoring and prediction.



Objectives of NMQObjectives of NMQCreate the infrastructure for communityCreate the infrastructure for community--wide research and wide research and development (R&D) of hydrometeorological applications in development (R&D) of hydrometeorological applications in support of monitoring and prediction of freshwater resources in support of monitoring and prediction of freshwater resources in the U.S. across a wide range of spacethe U.S. across a wide range of space--time scalestime scalesThrough the NMQ infrastructure, facilitate communityThrough the NMQ infrastructure, facilitate community--wide wide collaborative R&D and researchcollaborative R&D and research--toto--operations (RTO) of new operations (RTO) of new applications, techniques and approaches to precipitation applications, techniques and approaches to precipitation estimation (QPE), shortestimation (QPE), short--range precipitation forecasting (QPF), range precipitation forecasting (QPF), and severe weatherand severe weatherMaintain a scientifically sound, physically realistic realMaintain a scientifically sound, physically realistic real--time time system to develop and test techniques and methodologies for system to develop and test techniques and methodologies for physically realistic highphysically realistic high--resolution rendering of resolution rendering of hydrometeorological and meteorological processeshydrometeorological and meteorological processes

NMQ_xrt Polar Ingest NMQ_xrt Polar Ingest (1 km to 250 meter)(1 km to 250 meter)

Radar Data Sources



Polar Processing

Canadian Radar Network

WSR-88D

LDM

LDMFAA TDWR

NIDS L3FTP

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Product GenerationPrimary Server

Mosaic Servers

External Data Ingest

Cross Sections from 3Cross Sections from 3--D MosaicD Mosaic

Dallas Hail Storm, 5/5/1995

NMQ ‘Real Time’ CONUSNMQ ‘Real Time’ CONUS

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Snow/Rain Mix MS PCP (Dec. 11Snow/Rain Mix MS PCP (Dec. 11-- Jan. 1)Jan. 1)



NWS Water Science Vision: NWS Water Science Vision: Integrated Products and ServicesIntegrated Products and Services

CustomersNWS

NOAA

Federal Agencies

Tribal Agencies

State Agencies

Local Agencies

Private Sector

Academia

a) NWS-NDFD High-Resolution Gridded Water Resources Product Suite (WRPS)

The WRPS includes a comprehensive suite of high-resolution (1-10 km) gridded hydrologic state variable and flux datasets and derived products to support a wide range of future applications and services. Temporal characteristics of WRPS range from current-hour analyses to forecasts of several months. Datasets include rainfall, snowfall, snow water equivalent, snowpack temperature, snowmelt, soil moisture, soil temperature, evaporation, sublimation, streamflow, and surface storage. Other hydrologic variables such as groundwater, fuel moisture, soil stability (e.g. debris flows potential), water quality, etc. are also possible in this framework.

U.S.FOCUS

GLOBALCAPABILITY

ApplicationsDrought

Flood Management

Flash Flood Prediction

Water Supply

Transportation

Emergency Management

Agriculture

Debris Flows

Ecosystems Management

Research

• Snowpack• Precipitation

• Soil Moisture, Runoff• Evaporation• Groundwater• River flow• Surface Storage• Water Quality

•Others

From NWS Integrated Water Science Plan (2004)

NNMQMQNationalNational



QQuantitative uantitative PPrecipitation recipitation EEstimation and stimation and SSegregation egregation UUsing sing MMultiple ultiple SSensorsensors

Arizona/SRP Oklahoma/FAAAlabama/NASA

N&S Carolina/Sea Grant

Northeast Cooridor/FAAARTCC

LC-BoR

Colorado/FSL

NSSL/WISH 3D Mosaic and QPESUMS DeploymentsNSSL/WISH 3D Mosaic and QPESUMS Deployments

NSSL/WISH NMQ and nested ‘Micro’ NSSL/WISH NMQ and nested ‘Micro’ TestbedsTestbedsDeploymentsDeployments

SG/Tar River Estuary Model

Integration

ETL/Russian River HMT

BoR/MountianSnowfall

Assessment

Dual Polarization

‘‘Real Time’ CONUS Test BedReal Time’ CONUS Test Bed



NNMMQQ

33--D and 2D and 2--D MosaicD Mosaic

NNMMQ MotivationQ MotivationWeather systems span over multiple radar umbrellasWeather systems span over multiple radar umbrellas

Forecasters are often responsible for large warning areas that Forecasters are often responsible for large warning areas that require multiple radar coverage (e.g., NWS CWA, and FAA require multiple radar coverage (e.g., NWS CWA, and FAA ARTCC)ARTCC)

3D mosaic can facilitate:3D mosaic can facilitate:»» Better depiction of storm characteristics than 2D depictionsBetter depiction of storm characteristics than 2D depictions

»» Better understanding of microphysical processes leading to moreBetter understanding of microphysical processes leading to moreaccurate QPE and QPFaccurate QPE and QPF

»» 33--D data assimilation for stormD data assimilation for storm--scale numerical weather predictionscale numerical weather prediction»» Development of robust MS severe storm applications and Development of robust MS severe storm applications and

algorithmsalgorithms

NNMMQ ObjectivesQ ObjectivesTo create and to provide users with ‘real time’ 3D reflectivity To create and to provide users with ‘real time’ 3D reflectivity mosaic over conterminous USmosaic over conterminous US

–– Base data (levelBase data (level--II) ingest from NWS,II) ingest from NWS,FAA, Canadian and othersFAA, Canadian and others

–– Optimum and directed quality controlOptimum and directed quality controlof radar dataof radar data

–– Objective analysis is designed forObjective analysis is designed for»» Retaining highRetaining high--resolution info in raw dataresolution info in raw data»» Minimizing radarMinimizing radar--sampling artifactssampling artifacts

–– HighHigh--resolution: resolution: 1 km horizontal, 500m 1 km horizontal, 500m -- 1 km 21 vertical levels 1 km 21 vertical levels evolving to 250 meter horizontal and 35 vertical levelsevolving to 250 meter horizontal and 35 vertical levels

–– Rapid update: approx 5 minutes to 1 minute Rapid update: approx 5 minutes to 1 minute

NNMMQ ChallengesQ Challenges

Spatially nonSpatially non--uniform data resolutionuniform data resolution

NonNon--meteorological echo contamination/quality controlmeteorological echo contamination/quality control

Calibration differences among radarsCalibration differences among radars

Synchronization among radarsSynchronization among radars

Computational efficiency for realComputational efficiency for real--time applicationstime applications

Convective Case : 6/25/02, 2036ZConvective Case : 6/25/02, 2036ZKLOT and KIWXKLOT and KIWX

CREF_KLOT CREF_KIWX

Examples of Reflectivity QCExamples of Reflectivity QCBeforeBefore AfterAfter

KTLX 1999-05-04.0704Z

BeforeBefore AfterAfter

KFWS 1995-04-20.0453Z

Clear Air EchoesClear Air Echoes

Low intensityLow intensityShallow depthShallow depth

oo May not be segregated from very May not be segregated from very shallow stratiform precip/snow using shallow stratiform precip/snow using refl structure onlyrefl structure only

oo Require velocity infoRequire velocity info

AP EchoesAP EchoesLack of vertical continuityLack of vertical continuityRough textureRough texture

oo AP at far ranges can not be AP at far ranges can not be segregated from shallow precip by segregated from shallow precip by using refl structure onlyusing refl structure only

oo Need additional info such as satelliteNeed additional info such as satellite

BrightBright--Band Identification (BBID)Band Identification (BBID)((Gourley and Calvert, 2003Gourley and Calvert, 2003))

BB info will impact choice of objective analysis methods BB info will impact choice of objective analysis methods BBID steps:BBID steps:

–– 33--D Reflectivity FieldD Reflectivity Field–– Find Layer of Higher ReflectivityFind Layer of Higher Reflectivity–– Vertical Reflectivity GradientVertical Reflectivity Gradient–– Spatial/Temporal ContinuitySpatial/Temporal Continuity

33--D Spherical to Cartesian TransformationD Spherical to Cartesian Transformation(Zhang et al. 2003)(Zhang et al. 2003)

o oo

o+

No BB:Vertical linear interpolation

BB exists:Vertical and horizontal linear interpolation

BB

o

o

+No BB

Convective Case1: RHI, 263°Convective Case1: RHI, 263°

Raw Interpolated

Stratiform Case 2: RHI, 0°Stratiform Case 2: RHI, 0°

Raw Interpolated

Stratiform CaseStratiform CaseCAPPI at 2.3kmCAPPI at 2.3km

Raw Interpolated

Distance WeightingDistance Weighting

CREF_KLOT Mosaic CREF

Applications and Products Based upon the Applications and Products Based upon the 3D Mosaic3D Mosaic

2D and 3D products and Multi2D and 3D products and Multi--sensor Severe Storm Attributes sensor Severe Storm Attributes Composite Composite ReflRefl., Height of Comp. ., Height of Comp. ReflRefl..Hybrid Scan Hybrid Scan ReflRefl, Height of , Height of HybHyb scan scan reflrefl..ReflRefl on constant Ton constant T--levelslevelsGridded VILGridded VILGridded Hail productsGridded Hail productsEcho topEcho top

MultiMulti--sensor Quantitative Precipitation Estimation (in collaboration wsensor Quantitative Precipitation Estimation (in collaboration with ith OHD/NWS)OHD/NWS)–– HighHigh--resolution, rapid update precipitation accumulationsresolution, rapid update precipitation accumulations–– Short term QPFShort term QPF–– FlashFlash--flood detection and warningflood detection and warning

Data Assimilation for ConvectiveData Assimilation for Convective--scale Numerical Weather Prediction (in scale Numerical Weather Prediction (in collaboration with NCAR, FSL, CWB and NCEP)collaboration with NCAR, FSL, CWB and NCEP)–– 33--D diabatic initializationD diabatic initialization–– Reduce spinReduce spin--up time and improve convectiveup time and improve convective--scale QPFscale QPF

NMQ CurrentNMQ CurrentRadar Data Sources Processing

WSR-88D LDMQuickTime™ and aTIFF (Uncompressed) decompressor


Product Generation


Polar Ingest

3-D Mosaic

QPE

VerificationProduct Server

QPE LGC

NOAA Port/Other Sources

Computational TilesComputational Tiles

Real-time CONUS 3-D Reflectivity Mosaic

124+ Radars1 km x 1 km x 500m21 vertical levels5 min update cycle

NMQ FY 06 ActivitiesNMQ FY 06 ActivitiesQC improvements QC improvements –– GOES Satellite imagery and sounder data to remove APGOES Satellite imagery and sounder data to remove AP–– Diurnal variation of vertical reflectivity gradient (identificatDiurnal variation of vertical reflectivity gradient (identification of biological targets)ion of biological targets)–– Seasonal and geographical adaptive QC parameters Seasonal and geographical adaptive QC parameters

GapGap--fillingfilling–– Incorporate levelIncorporate level--III (NIDS) data when levelIII (NIDS) data when level--II data not availableII data not available–– Vertical Profile of Reflectivity (to fill data voids below lowesVertical Profile of Reflectivity (to fill data voids below lowest beams)t beams)–– Additional radars (e.g., Canadian radars, TDWR, CASA, and mobileAdditional radars (e.g., Canadian radars, TDWR, CASA, and mobile radarsradars))

Synchronization among individual radar scans and satellite imageSynchronization among individual radar scans and satellite imageryry

Improving and adding multiImproving and adding multi--sensor severe storm algorithmssensor severe storm algorithms

ShortShort--term Advection of reflectivity term Advection of reflectivity feildsfeilds

NMQ_xrtNMQ_xrt Polar Ingest Polar Ingest (1 km to 250 meter)(1 km to 250 meter)

Radar Data Sources



Polar Processing

Canadian Radar Network

WSR-88D

LDM

LDMFAA TDWR

NIDS L3FTP

LDMQuickTime™ and aTIFF (Uncompressed) decompressor


14 - rt2 - hs

QuickTime™ and aTIFF (LZW) decompressor


Product GenerationPrimary Server

Mosaic Servers


NMQ_XRTReal-time CONUS 3-D Mosaic

FY06124+ Radars1 km x 1 km x 500m21 vertical levels5 min updates cycle

FY07180+ Radars250 m x 250 m30 vertical levels<5 min update cycle

NMNMQQQuantitative Precipitation EstimationQuantitative Precipitation Estimation

Challenges to Quantitative Precipitation Challenges to Quantitative Precipitation Estimation (QPE) by RadarEstimation (QPE) by Radar

Reflectivity to Rainfall Conversion ProblemsReflectivity to Rainfall Conversion Problems–– Drop size distributionsDrop size distributions–– Mass fluxMass flux

Sampling ProblemsSampling Problems–– Anomalous propagationAnomalous propagation–– Ground clutterGround clutter–– Beam overshootingBeam overshooting–– MixedMixed--phase samplingphase sampling–– Hail contaminationHail contamination–– Bright band contaminationBright band contamination–– Radar coverage gaps (western US and coastal regions)Radar coverage gaps (western US and coastal regions)

Evolving QPE Evolving QPE Strategies and TechniquesStrategies and Techniques

1.1. Merging radar products with gauges and multisensor QPE Merging radar products with gauges and multisensor QPE --MPE MPE -- NWS OHDNWS OHD

2.2. SatelliteSatellite--based QPE (Hsu et al. 1996; Vicente et al. 1998)based QPE (Hsu et al. 1996; Vicente et al. 1998)3.3. Correction of accumulations by using a Vertical Profile of Correction of accumulations by using a Vertical Profile of

Reflectivity (VPR; Joss and Waldvogel 1970)Reflectivity (VPR; Joss and Waldvogel 1970)4.4. Use of DualUse of Dual--polarization variables (Ryzhkov et al. 1997)polarization variables (Ryzhkov et al. 1997)5.5. Multisensor QPE for the western US Multisensor QPE for the western US -- NSSL (Gourley et al. NSSL (Gourley et al.

2002)2002)

QQuantitative uantitative PPrecipitation recipitation EEstimation and stimation and SSegregation egregation UUsing sing MMultiple ultiple SSensorsensors

Satellite/Radar RegressionSatellite/Radar Regression

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Rad

ar R

ainr

ate

Sate

llite

CTT

Regression Equation

?

Generating Multisensor FieldGenerating Multisensor Field



QPE SUMS Rainfall Rate

Reg

ress

ion

Equa

tion

Sate

llite

CTT



Radar Only PCP (Dec. 11Radar Only PCP (Dec. 11-- Jan. 1)Jan. 1)

MS PCP (Dec. 11MS PCP (Dec. 11-- Jan. 1)Jan. 1)

Snow/Rain Mix MS PCP (Dec. 11Snow/Rain Mix MS PCP (Dec. 11-- Jan. 1)Jan. 1)

Current QPE SUMSCurrent QPE SUMS

Multisensor algorithm performs similarly to radarMultisensor algorithm performs similarly to radar--only for only for convective eventsconvective eventsDifferences arise where radarDifferences arise where radar--only estimates of only estimates of precipitation suffer:precipitation suffer:–– Complex terrainComplex terrain–– Stratiform precipitationStratiform precipitation–– Orographic precipitationOrographic precipitation

Multisensor approach offers ‘some’ hope in these “radar Multisensor approach offers ‘some’ hope in these “radar hostile” regimeshostile” regimes

Next Generation QPE ‘Q2’Next Generation QPE ‘Q2’Depart from radar centric precipitation typing to a true Depart from radar centric precipitation typing to a true multi sensor approach focused on 3multi sensor approach focused on 3--D mosaic grids of D mosaic grids of radar, satellite, model and surface observationsradar, satellite, model and surface observationsImprove logic for precipitation typing and maskingImprove logic for precipitation typing and maskingImplementation of robust gap filling and VPR adjustmentsImplementation of robust gap filling and VPR adjustmentsRobust synchronization of satellite imagery with radar for Robust synchronization of satellite imagery with radar for robust/ representative regressionsrobust/ representative regressionsIdentification and precipitation rate adjustments for Identification and precipitation rate adjustments for Orographic forced processes Orographic forced processes Optimization and mitigation of gage biasing latency Optimization and mitigation of gage biasing latency through parallel QPE processing.through parallel QPE processing.

Q2 TimetableQ2 Timetable

Initial version of Q2 running CONUS by Initial version of Q2 running CONUS by June 1, 2005June 1, 2005NSSL/OHD Q2 workshop June 20, 2005NSSL/OHD Q2 workshop June 20, 2005Phase out of QPESUMS by December 2005Phase out of QPESUMS by December 2005Q2 v 2.0 full implementation January 2006Q2 v 2.0 full implementation January 2006Short term QPF CONUS February 2006Short term QPF CONUS February 2006

NMQ Real Time VerificationNMQ Real Time Verification

National Mosaic System MonitoringNational Mosaic System Monitoring

Effects of Radar Calibration Effects of Radar Calibration DifferencesDifferences

Mosaics show Mosaics show boundaries in boundaries in QPE amounts QPE amounts from adjacent from adjacent radarsradars

500 ft. rule also 500 ft. rule also creates artifact creates artifact around KTLX around KTLX radarradar

NMQ QPE Performance ‘RT’ AssessmentNMQ QPE Performance ‘RT’ Assessment

Where do we go from here?Where do we go from here?

Joint Applications Development EnvironmentJoint Applications Development Environment(JADE)(JADE)

NMQ/JADENMQ/JADEJoint Applications Development EnvironmentJoint Applications Development Environment

Serve as national baseline for performance Serve as national baseline for performance assessment of QPE and QPF applicationsassessment of QPE and QPF applicationsWebWeb--based user interface based user interface Real time and archival applications testingReal time and archival applications testing–– Dedicated server; 10 TB RAID; DVD jukeboxDedicated server; 10 TB RAID; DVD jukebox

Variety of application platformsVariety of application platforms–– GEMPAK, McIDAS, ORPGGEMPAK, McIDAS, ORPG–– Verification statistics, data viewerVerification statistics, data viewer

Lead User Scenario: An ExampleLead User Scenario: An Example

UserApplications

ADAS or WRF 3DVAR Gridded

Analysis Fields

Visualization

Data Mining

WRF Model

UserApplications

UserApplications

Observational Data (GWSTB,

Other)

JADE componentsJADE components

Data access nodesData access nodesApplication I/O format Application I/O format Staging area for code testing and monitoringStaging area for code testing and monitoringDatabase managementDatabase managementGUI for archive playbackGUI for archive playbackVisualization tools (Visualization tools (ArcGISArcGIS, IDV?), IDV?)Validation tools (stats; difference fields)Validation tools (stats; difference fields)Forum (Forum (forum.nssl.noaa.govforum.nssl.noaa.gov))

NMQ JADE Goals and Science NMQ JADE Goals and Science ObjectivesObjectives

–– Develop a sustainable community hydrometeorological testbed for Develop a sustainable community hydrometeorological testbed for R&D of new QPE and shortR&D of new QPE and short--term QPF science and technology, with term QPF science and technology, with particular focus on water resources applicationsparticular focus on water resources applications

–– Expedite RTO of new science and technology through the testbed, Expedite RTO of new science and technology through the testbed, e.g., e.g., by facilitating testing and evaluation of QPE science for operatby facilitating testing and evaluation of QPE science for operational ional implementation in NEXRAD and the Advanced Weather Interactive implementation in NEXRAD and the Advanced Weather Interactive Processing System (AWIPS)Processing System (AWIPS)

–– Gain understanding necessary to develop radar and Gain understanding necessary to develop radar and multisensormultisensor QPE QPE methodologies capable of producing highmethodologies capable of producing high--resolution allresolution all--season, season, --region, and region, and ––terrain precipitation estimatesterrain precipitation estimates

–– Gain understanding necessary to integrate and assess new data soGain understanding necessary to integrate and assess new data sources urces from infrom in--situ, radar, and satellite observing systems, and methodologies situ, radar, and satellite observing systems, and methodologies and techniques to improve QPE in support of hydrology and water and techniques to improve QPE in support of hydrology and water resources and severe weather monitoring and prediction at the naresources and severe weather monitoring and prediction at the national tional scalescale

Radar

Satellite IR

Surface Obs

Upper Air Obs

Lightning

Model

DevelopmentEnvironment

INGESTQualityControl

Mapping

Operational:Applications& Systems

OperationalInfusionPathway

Assessment and Evaluation

Real timeVerification

JADE

NMQ/JADE TimetableNMQ/JADE Timetable

NMQ/JADE Workshop June 2005NMQ/JADE Workshop June 2005NMQ_XRTNMQ_XRT

»» Hardware/Server Procurement Hardware/Server Procurement -- February, 2005February, 2005»» Configuration and Deployment Configuration and Deployment -- March 15, 2005March 15, 2005»» System Testing System Testing -- April and May 2005April and May 2005»» Initial Product Generation Initial Product Generation -- June 2005June 2005

NMQ_JADENMQ_JADE»» Additional Servers Additional Servers -- August 2005August 2005»» JADE Environment Configuration JADE Environment Configuration -- Start Sept 2005Start Sept 2005

In ClosingIn Closing–– The NMQ project addresses highThe NMQ project addresses high--resolution resolution multisensormultisensor quantitative quantitative

precipitation estimation (QPE) for all seasons, regions and terrprecipitation estimation (QPE) for all seasons, regions and terrains in ains in support of hydrometeorological and hydrologic data assimilation support of hydrometeorological and hydrologic data assimilation and and distributed hydrologic modeling.distributed hydrologic modeling.

–– The NMQ system is being developed as a community testbed for R&DThe NMQ system is being developed as a community testbed for R&Dand RTO of QPE, shortand RTO of QPE, short--range QPF and severe weather science and range QPF and severe weather science and applications. It consists of the Research and Development Subsysapplications. It consists of the Research and Development Subsystem tem (RDS) and the Product Generation Subsystem (PGS).(RDS) and the Product Generation Subsystem (PGS).

–– To enable joint development, testing and evaluation in an open aTo enable joint development, testing and evaluation in an open and nd flexible environment, the Joint Applications Development flexible environment, the Joint Applications Development Environment (JADE) is being developed. The JADE configuration onEnvironment (JADE) is being developed. The JADE configuration onthe NMQ system is expected to become functional in the fall of 2the NMQ system is expected to become functional in the fall of 2005.005.

–– The many issues and complexities of quantitative estimation and The many issues and complexities of quantitative estimation and shortshort--range prediction of precipitation require a communityrange prediction of precipitation require a community--based approach based approach and effort. Toward building an open and flexible community testband effort. Toward building an open and flexible community testbed ed for QPE and other hydrometeorological applications, we invite for QPE and other hydrometeorological applications, we invite comments, suggestions, input, and participation of the communitycomments, suggestions, input, and participation of the community..

Thank you!Thank you!



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