A Rapid Prototyping Capability Experiment to A Rapid Prototyping Capability Experiment to Evaluate CrIS / ATMS Observations forEvaluate CrIS / ATMS Observations for

Urban Modeling ApplicationsUrban Modeling Applications

July 10, 2007July 10, 2007NASA Review of MRC RPCsNASA Review of MRC RPCs

Patrick J. Fitzpatrick Valentine G. Anantharaj Christopher M. HillPatrick J. Fitzpatrick Valentine G. Anantharaj Christopher M. Hill

Lori M. BruceLori M. Bruce J. Mark JanusJ. Mark JanusMississippi State UniversityMississippi State University

Lars-Peter RiishojgaardLars-Peter RiishojgaardGlobal Modeling and Assimilation Office /Global Modeling and Assimilation Office /

Joint Center for Satellite Data AssimilationJoint Center for Satellite Data Assimilation

Steve ChinSteve ChinLawrence Livermore National LaboratoryLawrence Livermore National Laboratory

General Overview

• NPOESS to be launched in 2013NPOESS to be launched in 2013

• preceding NPOESS Preparatory Project (NPP) preceding NPOESS Preparatory Project (NPP) to be launched in 2009to be launched in 2009

• component sensors of NPOESS and NPP to component sensors of NPOESS and NPP to include:include:

– Advanced Technology Microwave Sounder (ATMS)Advanced Technology Microwave Sounder (ATMS)

– Cross-track Infrared Sounder (CrIS)Cross-track Infrared Sounder (CrIS)

Sounder Overview

• ATMS– across track scanning microwave radiometer– cross-track resolution: 1.5 km

• CrIS– Fourier transform spectrometer – cross-track resolution: 14.0 km– vertical resolution: ~ 3.0 km

OSSE Methodology

Relatable Modeling Work

• MM5 simulations of squall line over Mississippi Delta region MM5 simulations of squall line over Mississippi Delta region (April 2005)(April 2005)

• Visible improvement in model representation of squall line with Visible improvement in model representation of squall line with the assimilation of radar datathe assimilation of radar data

Squall line with MM5 : 03-h forecast

w/ radar

cold start

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/ st

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Squall line with MM5 : 09-h forecast

w

/ st

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w

/ radarcold start

Squall line with MM5 : 12-h forecast

w/ radar

cold start

w

/ st

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Relatable Modeling Work

• Impact of radar data quantified through analysis of model background errorsImpact of radar data quantified through analysis of model background errors– errors computed between different forecast cycles errors computed between different forecast cycles (Parrish & Derber 1992)(Parrish & Derber 1992)

00UTC (Day 1) 12h forecast 24h forecast error

12UTC 12h forecast 24h forecast

error 00UTC (Day 2) 12h forecast

• within this RPC effort: within this RPC effort: – model background error analysis could be used to calibrate free-running model background error analysis could be used to calibrate free-running

simulations (e.g. with no verifiable boundary conditions) during assimilation of simulations (e.g. with no verifiable boundary conditions) during assimilation of data representing ATMS / CrIS data representing ATMS / CrIS

Configuration of Model Runs

ECMWF NR gridECMWF NR grid

• Run performed by ECMWFRun performed by ECMWF• Output administered by NCEPOutput administered by NCEP

• Global coverageGlobal coverage• T799 (T799 (∆x ~ 25 km)∆x ~ 25 km)

• Integration periods representing:Integration periods representing:– 27 September 2005 – 01 November 200527 September 2005 – 01 November 2005– 10 April 2006 – 03 May 200610 April 2006 – 03 May 2006

High-resolution Nature Run (HRNR)High-resolution Nature Run (HRNR)

Image adopted from Shuttle Radar Topography MissionImage adopted from Shuttle Radar Topography Mission

Configuration of Model Runs

ECMWF NR gridECMWF NR grid

Regional (MM5) NR gridRegional (MM5) NR grid

135135°W°W

4545°W°W

6060°N°N

00°°

Images adopted from Shuttle Radar Topography MissionImages adopted from Shuttle Radar Topography Mission

Configuration of Model Runs

Regional (MM5) NR gridRegional (MM5) NR grid

135135°W°W

4545°W°W

6060°N°N

00°°

• Run performed with MM5Run performed with MM5• ECMWF HRNR initial conditionsECMWF HRNR initial conditions

• broad domain centered over Gulfbroad domain centered over Gulf• ∆∆x ~ 9 km or 25 kmx ~ 9 km or 25 km

• integration periods as with HRNRintegration periods as with HRNR

Regional-Scale Nature Run (RSNR)Regional-Scale Nature Run (RSNR)

Image adopted from Shuttle Radar Topography MissionImage adopted from Shuttle Radar Topography Mission

Configuration of Model Runs

Image adopted from Shuttle Radar Topography MissionImage adopted from Shuttle Radar Topography Mission

Regional (MM5) NR gridRegional (MM5) NR grid

135135°W°W

4545°W°W

6060°N°N

00°°

Approximate Approximate Nested WRF gridNested WRF grid

• Runs performed with WRFRuns performed with WRF• RSNR as initial conditionsRSNR as initial conditions

• ∆∆x ~ 3 km or 9 kmx ~ 3 km or 9 km• domain focused on Gulfdomain focused on Gulf

• integration period of 48 hintegration period of 48 h

• focus on mesoscale weather focus on mesoscale weather systemsystem

Sensitivity experimentsSensitivity experiments

Houston / GalvestonHouston / Galveston

North Central Gulf North Central Gulf CoastCoast

Tampa Bay regionTampa Bay region

Urban center study candidates

Source: U.S. Census 2006 estimatesSource: U.S. Census 2006 estimates North Central Gulf CoastNorth Central Gulf Coast

Tampa Bay regionTampa Bay region

Houston / GalvestonHouston / Galveston

Current RPC Plan

• WRF sensitivity experimentsWRF sensitivity experiments– exclusion (or denial) of datasets for assimilationexclusion (or denial) of datasets for assimilation

– inclusion of data representing ATMS / CrISinclusion of data representing ATMS / CrIS

– assess impact of ATMS / CrIS on modeling of urban centerassess impact of ATMS / CrIS on modeling of urban center

• Implement urban canopy parameterization (UCP) scheme from Implement urban canopy parameterization (UCP) scheme from Lawrence Livermore National Laboratory (LLNL)Lawrence Livermore National Laboratory (LLNL)– try MODIS and ASTER data in place of USGS land surface data try MODIS and ASTER data in place of USGS land surface data

within the UCP scheme within the UCP scheme

• Compute maps of aerodynamic roughness within urban Compute maps of aerodynamic roughness within urban environmentenvironment– use SRTM topography for simulations of large-eddy scale (LES)use SRTM topography for simulations of large-eddy scale (LES)

LLNL UCP scheme

• allows better representation of allows better representation of urban heat island, other effects urban heat island, other effects of urban infrastructure in of urban infrastructure in mesoscale modelmesoscale model

• Currently uses USGS emissivity Currently uses USGS emissivity data for input parameters of data for input parameters of schemescheme

• use of MODIS and ASTER data use of MODIS and ASTER data may allow more realistic may allow more realistic representation of urban canopyrepresentation of urban canopy

COAMPS® tracer concentrations (from Chin et al. 2000)

Contours – modeled; Shades – observed

Aerodynamic Roughness EstimationAerodynamic Roughness Estimation

• GoalsGoals– develop robust (general, efficient, accurate) aerodynamic roughness “distribution” develop robust (general, efficient, accurate) aerodynamic roughness “distribution”

estimatorestimator– investigate aerodynamic roughness parametric sensitivityinvestigate aerodynamic roughness parametric sensitivity– investigate uncertainty propagationinvestigate uncertainty propagation

• Computational ApproachComputational Approach– RANS/LES with 2RANS/LES with 2ndnd order turbulence closure (based on SimCenter U order turbulence closure (based on SimCenter U22NCLE)NCLE)– discrete roughness elements – Taylor, et al.discrete roughness elements – Taylor, et al.– parametric sensitivity analysis using complex Taylor series – Newman, III, et al.parametric sensitivity analysis using complex Taylor series – Newman, III, et al.

• Investigative Approach (verification and validation)Investigative Approach (verification and validation)– isolated roughness element simulation isolated roughness element simulation – roughness element array simulationroughness element array simulation– random roughness element simulationrandom roughness element simulation– urban simulationurban simulation

Current Research by M. JanusCurrent Research by M. Janus

Science Models

numerical weather predictionmodels (MM5, WRF)

3DVAR assimilation

Monitoring andMeasurements

satellite sensors (ATMS and CrIS)

conventional data (METAR, buoys, rawinsondes)

Decision SupportTools / Systems

Prediction ofregion-specificweather events

Local threat orbenefit assessment

Risk communication,mitigation

Valuesand Benefitsto Society

Inputs Outputs Outcomes Impacts

Quantify impact of ATMS andCrIS on weather forecasting

Ensure citizen welfare, improveemergency preparedness and

planning

Improve regional weatherforecasting with

increasing accuracy andlonger lead times

temperature, moisture,pressure

observatio

ns

predictions

weatherforecasting

refinedthermodynamic

profile

Solutions: Science to Decision Support – Disaster PreparednessWeather Pr