Unlocking the Scientific Value of Unlocking the Scientific Value of NEXRAD Weather Radar DataNEXRAD Weather Radar Data

Witold F. KrajewskiWitold F. Krajewskiwithwith

Anton Kruger, Ramon Lawrence, Allen Anton Kruger, Ramon Lawrence, Allen A. Bradley, and Grzegorz J. Ciach A. Bradley, and Grzegorz J. Ciach

Two Issues: Two Issues:

• Hydrologic use of NEXRAD Hydrologic use of NEXRAD data: data: (NSF funded NEXRAD Hydro-ITR (NSF funded NEXRAD Hydro-ITR project)project)

• Probabilistic QPE Probabilistic QPE (OHD initiative (OHD initiative towards ensemble based hydrologic towards ensemble based hydrologic prediction)prediction)

NEXRAD Hydro-ITR NEXRAD Hydro-ITR ProjectProject

• The University of Iowa (Lead)The University of Iowa (Lead)– W.F. Krajewski (PI)W.F. Krajewski (PI)– A.A. Bradley, A. Kruger, R.E. LawrenceA.A. Bradley, A. Kruger, R.E. Lawrence

• Princeton UniversityPrinceton University– J.A. SmithJ.A. Smith– M. Steiner, M.L.BaeckM. Steiner, M.L.Baeck

• National Climatic Data CenterNational Climatic Data Center– S.A. DelgrecoS.A. Delgreco– S. AnsariS. Ansari

• UCAR/Unidata Program CenterUCAR/Unidata Program Center– M. K. RamamurthyM. K. Ramamurthy– W.J. WeberW.J. Weber

Project PremiseProject Premise

• Rainfall is a key component of the Rainfall is a key component of the hydrologic cyclehydrologic cycle

• NEXRAD data have potential to NEXRAD data have potential to provide surface rainfall estimatesprovide surface rainfall estimates

• Reality: NEXRAD data are severely Reality: NEXRAD data are severely underutilized in the hydrologic underutilized in the hydrologic sciencessciences

Why?Why?

These are significant obstacles – often These are significant obstacles – often show-stoppers.show-stoppers.

• Weather radar operationsWeather radar operations• Radar data quality controlRadar data quality control• Formatting and data handlingFormatting and data handling• Radar-rainfall algorithmsRadar-rainfall algorithms

Current methods of accessing NEXRAD Current methods of accessing NEXRAD data require considerable expertise in:data require considerable expertise in:

Project GoalProject Goal

……to provide the science (hydrologic) community to provide the science (hydrologic) community with ready access to the vast archives and real-with ready access to the vast archives and real-time information collected by the national network time information collected by the national network of NEXRAD radars.of NEXRAD radars.

AgainAgain

The main focus is on radar-rainfall data for use in The main focus is on radar-rainfall data for use in hydrology, hydrometeorology, and water hydrology, hydrometeorology, and water resources. resources.

What Does This Mean?What Does This Mean?

““Find all the 2002 storms over the Find all the 2002 storms over the Ralston Ralston Creek watershed Creek watershed with with mean arealmean areal precipitationprecipitation greater than greater than X mmX mm, and with a , and with a spatial extent of more than Z kmspatial extent of more than Z km22, with a , with a duration of less than N hoursduration of less than N hours. I want the . I want the data in data in GeoTIFFGeoTIFF””

Rather than saying:Rather than saying:

““Get the Level II data for the KDVN Iowa Get the Level II data for the KDVN Iowa NEXRAD (KDVN) for the 16 July 2002 severe NEXRAD (KDVN) for the 16 July 2002 severe weather outbreak. Show a 2 km CAPPI of weather outbreak. Show a 2 km CAPPI of reflectivity and cross-section of Doppler reflectivity and cross-section of Doppler velocity”velocity”

a hydrologist wants to saya hydrologist wants to say

Hydrology Centered View

• Basin-centered– Name, USGS HUC, etc.

• Precipitation– MAP, Rain amount, … not Reflectivity Z

• Georeferencing– Location, spatial extent

• Data Format– GeoTIFF, NetCDF => use in GIS

“FindFind all the 2002 storms over the Ralston Creek watershed with mean areal precipitation greater than X mm, and with a spatial extent of more than Z km2, with a duration of less than N hours. I want the data in GeoTIFF”

Encode expertise in software systemEncode expertise in software system

IT IssuesIT Issues– OpenOpen source vs. commercial software, Java– Data formats: NetCDF & HDF– Front end/client & back end /server– Linux vs. Windows– XML, XML Schema, OWL– Metadata standards (Federal, USGS)– Interfaces with DLESE, NSDL, THREDDS– LDM/IDD– Web services: SOAP, XML-RPC– Relational Databases– Compatibility with (ESRI) GIS

Compute Engine

Metadata Archive

CUAHSI HISData Archive

Unidata

Extreme Events

University A

Runoff Model

Data Archive

Metadata Archive

NCDC

Injects NEXRAD Data

University B

Internet

Request Request

User/Client’s ViewUser/Client’s View

User/Client

Program Library

Get data

Connect and query

Get URIs

HTTP

Data Archive

Metadata Archive

NCDC

Metadata Archive

CUAHSI HIS

““Find all the 2002 storms over the Find all the 2002 storms over the Ralston Ralston Creek watershed Creek watershed with with mean arealmean areal precipitationprecipitation greater than greater than X mmX mm, and with a , and with a spatial extent of more than Z kmspatial extent of more than Z km22, with a , with a duration of less than N hoursduration of less than N hours. I want the . I want the data in data in GeoTIFFGeoTIFF””

Concept: Metadata

• Metadata– Data about data– Descriptive statistics

• Areal coverage, Maximum, Minimum, AP present, Associated Hydrologic Units, Anything else

• Key Ideas– Simple, easy to compute– Do not have to be definitive– Building blocks for other metadata

Rainfall AlgorithmsRainfall Algorithms

• NWS Precipitation Processing SystemNWS Precipitation Processing System• Anomalous propagation and ground clutter echo Anomalous propagation and ground clutter echo

detection and removaldetection and removal• Range-dependent bias adjustmentRange-dependent bias adjustment• Reflectivity vs. rainfall rate relationshipReflectivity vs. rainfall rate relationship• Coordinate conversionCoordinate conversion• Advection correctionAdvection correction• Accumulation calculationAccumulation calculation• Multiple radar mosaicingMultiple radar mosaicing• Combining with rain gauge dataCombining with rain gauge data• Uncertainty quantificationUncertainty quantification• Etc., etc….Etc., etc….

• Embedded expertise Embedded expertise • Will range from simple to complexWill range from simple to complex

PPS PQPE ProjectPPS PQPE Project

• The University of IowaThe University of Iowa– W.F. KrajewskiW.F. Krajewski– Grzegorz J. Ciach, Gabriele Grzegorz J. Ciach, Gabriele

VillariniVillarini• National Weather Service OHDNational Weather Service OHD

– David KitzmillerDavid Kitzmiller– Richard FultonRichard Fulton

• NSSLNSSL– Alexander RyzhkovAlexander Ryzhkov– Dusan ZrniDusan Zrničč

• Hydrologic Research CenterHydrologic Research Center– Konstantine P. GeorgakakosKonstantine P. Georgakakos

Product-Error Driven Product-Error Driven ApproachApproach

• Collect reliable data on the relation Collect reliable data on the relation between different radar-rainfall (RR) between different radar-rainfall (RR) products and the corresponding products and the corresponding True True RainfallRainfall;;

• Create a flexible model of this relation and Create a flexible model of this relation and apply it to the PQPE product generator;apply it to the PQPE product generator;

• Develop empirically based generalizations Develop empirically based generalizations of the model for different situations.of the model for different situations.

Combined effects of all error sources!Combined effects of all error sources!

Ground Reference Error FilteringGround Reference Error Filtering

• Assume that, for given spatio-temporal Assume that, for given spatio-temporal resolution and radar-range, we have resolution and radar-range, we have available:available:– Large sample of corresponding (Large sample of corresponding (RRrr ,R ,Rgg) )

pairs;pairs;– Detailed information about spatial rainfall Detailed information about spatial rainfall

variability in this sample.variability in this sample.

• Can we retrieve a good estimate of the Can we retrieve a good estimate of the verification distribution (verification distribution (RRrr , R , Raa)?)?

ARS ARS MicronetMicronet

Oklahoma PicoNetOklahoma PicoNet

G/R Quantiles: Hourly ScaleG/R Quantiles: Hourly Scale

Hot All

WarmCold

Radar-Rainfall (mm)Radar-Rainfall (mm)

Con

ditio

nal M

ultip

licat

ive

Err

or

10%10%

25%25%

50%50%

75%75%

90%90%

Model Fitting: Hot SeasonModel Fitting: Hot SeasonC

ondi

tiona

l Mul

tiplic

ativ

e S

tand

ard

Dev

iatio

n

Radar-Rainfall (mm)Radar-Rainfall (mm)

Temporal Correlation of the Random ComponentTemporal Correlation of the Random Component(hourly scale)(hourly scale)

Co

rre

latio

n C

oef

ficie

nt

Co

rre

latio

n C

oef

ficie

nt

Lag (minutes)Lag (minutes)

Cold Warm

Hot All

Conclusions & Recommendations Conclusions & Recommendations

Guiding principles for solving the PQPE Guiding principles for solving the PQPE problem:problem:• Nested clusters of double gauges strategically Nested clusters of double gauges strategically

located to represent different rain regimes of the located to represent different rain regimes of the countrycountry

• Cluster configuration designed for specific Cluster configuration designed for specific purpose (e.g. statistical characterization of purpose (e.g. statistical characterization of rainfall, minimum RMS, spatial dependence of rainfall, minimum RMS, spatial dependence of errors, etc.)errors, etc.)

• Development of inference methodologies and Development of inference methodologies and transferability studiestransferability studies

• Large sample (5-10 years)Large sample (5-10 years)• High quality of data = double gauge setup!High quality of data = double gauge setup!

Thank You! Thank You!

The EndThe End