Kurt D. Hondl NSSL Radar Research & Development Division [email protected] Real-Time Radar Processing at the National Severe Storms Laboratory

Kurt D. HondlNSSL Radar Research & Development Division

[email protected]

Real-Time Radar Processing at the National Severe

Storms Laboratory

• What capabilities have been developed at NSSL that could support NOAA’s NextGen effort?

An integrated, real-time, multi-sensor platform to develop, test, and assess advanced techniques in quality control, data integration, application development (in severe weather and precipitation estimation), display, and short-term forecasting

A 3D/4D grid of radar (and other observations) and derived severe weather & precipitation estimation products

2NextGen System of Systems Workshop (Hondl / NSSL) 6-7 Oct 2009 2

NMQ & WDSS-II

NMQ & WDSS-II

The result of 10+ years of research, application development, and operational testing at NSSL & NWS Forecast Offices

NextGen System of Systems Workshop (Hondl / NSSL) 6-7 Oct 2009 33

http://nmq.ou.edu

NMQ: National mosaic and Multi-sensor QPE

NMQ: National mosaic and Multi-sensor QPE

• A suite of quantitative precipitation estimation algorithms

• Uses multi-sensor observations

• Products delivered to RFCs (AHIPS) and NWS FOs (FFMPA) for operational use

• A 2-yr online archive a precipitation products

NMQ Precipitation Products

• Reflectivity ProductsVertical Profile of Reflectivity, Bright-band IdentificationHybrid-Scan Reflectivity (VPR corrected)

• Precipitation Products1-HR Precip, 3-HR, 6-HR, 12-HR, 24-HR, and 72-HR PrecipRadar only, Multi-Sensor, Radar with Gauge Bias CorrectionLocal Gauge BiasGauge-only Precip

• Precipitation VerificationComparison to Stage-II, Stage-III, Stage-IV, HADS, MPE

References Vasiloff, S., D.J., Seo, K. Howard, J. Zhang, D. H. Kitzmiller, M. G. Mullusky, W. F. Krajewski, E. A. Brandes, R. M. Rabin, D. S. Berkowitz, H. E. Brooks, J. A. McGinley, R. J. Kuligowski, and B. G. Brown., 2007: Q2: Next generation QPE and very short-term QPF. Bull. Amer. Met. Soc. 88, 1899-1911.

4NextGen System of Systems Workshop (Hondl / NSSL) 6-7 Oct 2009

55

http://wdssii.nssl.noaa.gov

WDSS-II: Warning Decision Support System –

Integrated Information

WDSS-II: Warning Decision Support System –

Integrated Information

• A suite of data ingest and quality control processes

• Severe weather algorithms and applications

• Uses multi-sensor observations

• Products delivered to NCEP (SPC and AWC) and NWS FOs (OUN,FTW,TUL), CAPS, ESRL, NCAR and Environment Canada for operational use

• An archive of automated Hail Swath and Rotation Track information

NextGen System of Systems Workshop (Hondl / NSSL) 6-7 Oct 2009

WDSS-II Severe Weather Algorithms

• 3D Reflectivity MosaicAutomated QC Neural NetworkVIL, EchoTop, Composite, IsothermsHail (Max Expected Size, Probability, Hail Swath)Kmeans/Segmotion Nowcast, Storm TrackingStorm Classification

• 3D VelocityVortex Detection & Diagnosis Algorithm (from Azimuthal Shear)Multi-Doppler Wind Analysis

• Multi-Sensor AlgorithmsLightning (NLDN & LMA)Near Storm Environment (from RUC numerical model analyses)Satellite


NSSL 3D/4D Radar System

3D Radar Merger productIngesting data from national network of WSR-88D radarsGrid resolution of 1km x 1km over the CONUS (6200km x 3000km x 20km) with 31 vertical levelsData streaming in from multiple radars and go through an automated QC to remove non-precipitation echoesIntegrate with other meteorological data to generate algorithm products (satellite, RUC model analysis, etc)3D grid and products updated every 5 minutes

References– Lakshmanan, V., T. Smith, K. Hondl, G. Stumpf, A. Witt, 2006: A Real-Time, Three

Dimensional, Rapidly Updating, Heterogeneous Radar Merger Technique for Reflectivity, Velocity and Derived Products. Weather and Forecasting, 21, 802-823.

– Langston, C., J. Zhang, and K. Howard, 2007: Four-Dimensional Dynamic Radar Mosaic. J. Atmos. Oceanic Technol., 24, 776 790.

– Zhang, J., K. Howard, and J.J. Gourley, 2005: Constructing three-dimensionalmultiple radar reflectivity mosaics: examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol., 22, 30-42.


WSR-88D Radar Mosaicand Products

Generating 1-km CONUS products every 5-minRequires ~22 servers to produce 1-km CONUS 3D radar observation grid every 5-minAnother ~10 servers to produce WDSS-II severe weather products and ingest multi-sensor dataAnother ~16 servers to produce NMQ precipitation estimate products

Expanding Linux server farm to add capacityWill eventually produce 0.5-km CONUS products every 2-min to take advantage of WSR-88D Super ResolutionWill be adding Dual Polarization algorithms/products as the WSR-88D network is upgraded


CASA-KLWE

Canadian Radars

TDWR-OKC

TV Radars-KPIX

Other Radars

Adding other radars to the 3D/4D radar observations grid will help fill-in coverage gaps not sampled by the WSR-88D or

augment observations when maintenance or outages prevents WSR-88D observations.


NMQ/WDSS-II Domain

~150 WSR-88D 31 Canadian 2 TDWR 1 TV station radar


NMQ/WDSSIIAviation,

Severe weather Precipitation

ProductsAnd Diagnostics

11

NMQ/WDSS-II Collaborators and Product Distribution

NextGen System of Systems Workshop (Hondl / NSSL) 6-7 Oct 2009

NMQ/WDSS-IIComputational Infrastructure

The entire NMQ/WDSS-II processing system is composed of 2 Quad-Core AMD Opteron Processors (2.9 GHz with 32GB RAM) Linux servers from a single manufacturer (HP);

Servers and server drives can quickly be configured, maintained and swapped out;

Running RedHat 64-bit OS;


Radar Data Ingest• Ingest base level 2 data from 158 tri-agency (DOC, DOD, DOT) 10 cm WSR-88D radars

• Ingest base level data from 33 5-cm radars from Environmental Canada/NCDC

• Ingest base level data from two TDWRs using direct connections

• Ingest commercial radars in addition to experimental radar systems such as CASA and PAR

• System designed to accommodate increasing access to radar systems and radar networks

WSR-88D

TDWR

Canadian Radars


NMQ/WDSS-IIMosaic and Product Creation

Radar Ingest

4D grids & products Precipitation Products

Severe Weather Products

Aviation NextGen Weather Products

Products are disseminated in NetCDF, binary, AWIPS, N-AWIPS, GIS, and HRAP formats using the LDM protocol.

WSR-88D

TDWR

Canadian


Multi-sensor Ingest

NMQ/WDSS-IIMulti-Sensor Inputs

Ingest and process other sources of information to improve products

Radar data (as previously described)

Satellite

NWP data (RUC model)

Lightning (NLDN, LMA)

Surface Observations (ASOS, Mesonets)

Rain Gauge



NMQ/WDSS-II NextGen Product Generation

2012 to 2016 Current to 2012



2012 to 2016 Current to 2012



2012 to 2016 Current to 2012


4D Radar Mosaic Development Pathway

FY10: NCEP Implementation of CONUS 4D reflectivity mosaic and products– Severe weather and aviation related products per NextGen requirements – Resolution of 1.0-km x 2-min update cycle with 3D reflectivity mosaic 31 levels– Ingest includes commercial, Canadian radars and TDWRS for comparison

• Evaluation of data quality concerns and attenuation at shorter wavelengths– Q2 precipitation type and rate products

FY11: Operational 0.5-km CONUS 4D reflectivity mosaics and products– Improved automated quality control methods for TDWR, Canadian and commercial

radars– Initial 3D Mosaics for Hawaii / Alaska regions– Evaluating and advancing Dual-Pol algorithms specific to NextGen requirements

FY12: Dual Polarization 4D CONUS Mosaics prototype at NSSL– Initial 4D Dual Polarization Hydrometeor CONUS mosaic for model data assimilation– Advanced Dual polarizing quality control and hydrometer classification

FY13: Operational Dual Polarization 4D CONUS Mosaics at NCEP– Initial connections to Mexican & Caribbean radars


4D Radar Mosaic Development Pathway

FY14: North America 4D Mosaics prototype at NSSL– Inclusion of additional gap filling radars in 4D Mosaic (Commercial, CASA)– Mexican & Caribbean radars in 4D Mosaic

FY15: Operational North America 4D Mosaics at NCEP– Integrate airborne radars (e.g. Hurricane observations) into the 4D radar cube– Developing strategies for 4-D radar mosaic of MPAR radar data– Initial radar 3D mosaics from European radar networks– Initial radar 3D mosaics from Asian radar networks

FY16: Hemispheric 4D reflectivity Mosaics prototype at NSSL – Prototype the utilization of airborne radars as node functions to autonomously exchange radar

with surrounding aircraft and ground systems as well as into NCEP’s 4D dynamic mosaic

FY18: Hemispheric 4D Radar Mosaics at NCEP– Initial Northern Hemispheric radar mosaics– Initial hemispheric precipitation and storm type mosaic combining satellite and radar

FY20: Hemispheric 4D Radar Mosaics and Multi-sensor Aviation products at NCEPPrototype combined surface, airborne, space radars observations

Initial inclusion of MPAR for 4D mosaics


2008 2009 2010 2011 2012 2013 2014 2015

2016

2017

2018

2019

2020

2021

2022

WSR88D Dual Polarization

WSR88D High Res

Canada Network Commercial Radars

1-km/5-min 1-km/2-min 500-m/2-min 500-m/1-min 250-m/1-min

North America Resolution and Refresh Rate

WSR88D DoD L2

MPAR

TDWR Network

CASA

Mexico Network

Caribbean Network

Seamless Integration of Radar Advances, Systems, and NetworksSeamless Integration of Radar Advances, Systems, and Networks

NMQ/WDSSII NextGen Future Research Activities

NMQ/WDSS-II Summary

• NSSL continues to facilitate the seamless, scientifically sound, high resolution integration of radar systems and networks into a 4D frame as the basis for NextGen radar authority

• The experimental system serves as the foundation for other aviation research and product development

• Completed the initial operational infusion at NCEP for model data assimilation and CONUS, high resolution multi sensor radar based products.

• In FY10, NSSL will establish at NCEP a stand alone NextGen radar 4D cube as the foundation to facilitate radar derived products to meet FAA NextGen requirements.


Documents

Kurt D. Hondl NSSL Radar Research & Development Division [email protected] Real-Time Radar Processing at the National Severe Storms Laboratory