workshop on hyperspectal meteorological science of uw-muri and beyond 14–15 may 2002, madison,...

Workshop on Hyperspectal Meteorological Science of UW-MURI and Beyond14–15 May 2002, Madison, Wisconsin

Numerical Weather Prediction Numerical Weather Prediction Modeling for MURI/AtmosphericModeling for MURI/Atmospheric

Parameter RetrievalsParameter RetrievalsJohn R. Mecikalski, Derek J. PosseltJohn R. Mecikalski, Derek J. Posselt

CIMSS Co-InvestigatorsCIMSS Co-Investigators

1. Overview of NWP support2. GIFTS Simulated Data for Algorithm &

Product Development3. Computational Requirements4. Atmospheric Parameter Retrievals

O U T L I N E


NWP Infrastructure at CIMSSNWP Infrastructure at CIMSS

• PSU/NCAR MM5

• UW-Nonhydrostatic Modeling System (UW-NMS)

• Weather Research & Forecasting (WRF)

• Rapid Update Cycle-2 (RUC2)

The capabilities of numerically simulating the atmosphere overa wide range of meteorological scales, over large geographicaldomains, and for realtime numerical weather prediction (NWP)are rapidly increasing at CIMSS.


NWP support for Ongoing ProjectsNWP support for Ongoing Projects

• Cloud-Radiative modeling for instrument validation, radiance and retrieval algorithm development (MURI PI & Co-I’s)

• Generate “truth” atmosphere for satellite-based estimates of PBL stability, and convective initiation studies (MURI Co-I’s & MURI PM)

• Assessment of turbulence with NAST-I data (MURI PM)


The UW-NMS and PSU/NCAR MM5The UW-NMS and PSU/NCAR MM5

• A robust NWP system:– scaleable– explicit physics at 4 km-resolution– explicit microphysics for accurate clouds– variably-stepped topography

• Excellent model for “Cloud-Radiative” experiments: Independent of other NWP systems

• Developed for multi-processor, distributed memory computational environments (Fortran-90 with MPI).


NWP for Retrieval & SimulationNWP for Retrieval & Simulation

• NWP can provide atmospheric retrieval algorithms critical “first guess” information.

• NWP for direct GIFTS data and instrument simulation:– A numerically simulated atmosphere is

considered “nature” and is assumed to very accurately represent the true state.

– Requires a sophisticated numerical model and is therefore computationally very expensive(1 Gflops per data “cube”)


Improving Retrieval First GuessImproving Retrieval First GuessAERI (red) versus Radiosonde (black)AERI (red) versus Radiosonde (black)

First Guess

Temperature

Dew PointTemperature


Alt

itu

de

(k

m)

• April 1998-October 1999,463 AERI+Model/radiosonde profiles

• Differences are less than 1 deg K

• AERI+Model every 10minutes, sondes every3-12 hours

AERI

ETA MODEL

AERI/MODEL - RADIOSONDE (K)

NWP in Atmospheric Retrievals:NWP in Atmospheric Retrievals:AERI+Model/Radiosonde Temperature ComparisonAERI+Model/Radiosonde Temperature Comparison

NWPMODEL

GIFTS


Simulating GIFTS DataSimulating GIFTS Data

High-resolution numerical simulations are used to provide the following atmospheric parameters to the GIFTS radiative-transfer model:– Temperature– Water vapor mixing ratio– Mixing ratios and mean particle

diameters of cloud and ice liquid water– Liquid and ice water path– Cloud-top height with respect to both

liquid and ice cloud

Goal: To provide investigators with simulated (interferogram) data that accurately represents what will eventually come from the GIFTS instrument.


Numerical Model Output toSimulated Radiances

Simulated GIFTS Data Cube

Simulated GIFTS Brightness Temperatures

NWP Data Cube

Clouds from UW-NMS

ForwardModel:

Dave Tobin

ForwardModel:

Dave Tobin


Derived GIFTS DataDerived GIFTS Data

• Vertical Temperature Profiles• 1 sounding per 1 scene pixel • 128 x 128 = 16384 scene pixels• 4 km pixel spatial resolution (nadir)

“Regional Sounding” Product:• 100 vertical layers• Retrieved values/cube = 128x128x100• 1.6 million retrieved values/cube• 10 second dwell time

Longitude (deg)

Lat

itud

e (d

eg)

Tem

pera

ture

(K

)

GIFTS SIMULATED TEMPERATURE DATA CUBE950 MB LAYER TEMPERATURE

GIFTS SIMULATED VERTICAL TEMPERATURE PROFILE

Pre

ssu

re (

mb

)

Air Temperature (K)


Modeling Infrastructure for Large ScaleModeling Infrastructure for Large ScaleGIFTS-IOMI data processingGIFTS-IOMI data processing

• Need to simulate GIFTS data at 4 x 4 km resolution over large domain:– Distributed memory, massively parallel computer code

and computer system– Must be accomplished in a timely manner [O(few

days)]– Demands a sophisticated atmospheric model (UW-

NMS)

• Combination of UW-NMS and MM5 allows us to simulate “large” regional domains.


• Large-Scale Symmetric Multi-Processor (SMP)– Integrated unit with high-bandwidth backplane– Shared RAM, multiple CPU, single OS kernel– Communication: shared memory & semaphores– Examples: SGI Origin, IBM RS/6000

• Linux Cluster– Network of inexpensive COTS computers– Multiple RAM, multiple CPU, multiple OS kernel– Communication: TCP/IP, sockets & datagrams– Examples: Sandia ‘CPlant’, Forecast Systems Laboratory ‘Jet’

• 64-bit Linux

Necessary Computational Infrastructure Necessary Computational Infrastructure for MURI: for MURI: SMP & Cluster SystemsSMP & Cluster Systems


Hardware Diagram: Hardware Diagram: Linux Cluster

Network Switch

UPS

TapeArchive

FileserverComputer

DiskArray

Rack andCPUs

70% of the initial cost30% of the initial cost


Current Computational LimitsCurrent Computational Limits

3 x 3 Cube Domain:• 4 km resolution• Number of grid points:

400 x 400 x 40 = 6400000• Approximate memory use:

15-20 Gb of RAM• Total Cluster memory:

24 Gb of RAM


Near-Term LimitsNear-Term Limits


640 x 640 x 40 = 16384000• Anticipated memory use:

45-50 Gb of RAM• 16 more processors

(8 more nodes)• 32-bit limit for domain set-up


Future NeedsFuture Needs


3200 x 3200 x 40 = 4.1x108

• Approximate memory use:1170 Gb of RAM

• Doable?


Minimum Calculation Times forMinimum Calculation Times for3 by 3 Simulation (3 time steps)3 by 3 Simulation (3 time steps)

Processing Steps Time on 16 CPU Cluster

Numerical weather model calculations 10 days

Fast model coefficient generation 18.5 days *

Calculation of TOA radiances 5 days

FTS optics simulation 3 days

Detector array modeling and linear calibration 0.5 days

Total 37 days

* Not parallelized yet


Atmospheric Parameter RetrievalsAtmospheric Parameter Retrievals

Progress to Date: June 2002–May 2003• Temperature and Water Vapor

– Jun Li

• First Winds– Chris Velden, Gail Dengel

• Stability Indices– Wayne Feltz, John Mecikalski

• Atmospheric (PBL) Turbulence– John Mecikalski, Ryan Torn, Wayne Feltz

• Visibility: “GVision”– Derek Posselt, Wayne Feltz, John Mecikalski, Tom Rink


Atmospheric Temperature, Moisture, OzoneAtmospheric Temperature, Moisture, Ozone

1. The Physical retrieval model has been developed

2. Two fast ways for Jacobian calculation are developed

3. Contrast between surface skin temperature and surface air temperature on boundary layer moisture

4. Simulation studies using cube data from MM5

Clear Sky retrieval of T


First WindsFirst Winds

Issues for Winds …

Critical need for LARGE “cube” simulation data sets; without such large domains, useful wind sets from simulated GIFTS data are not possible

– First “GIFTS” winds have been produced.

– Issues of cube numbers when retrieving winds (2x2 or larger)

– Issues of concatenating cubes


Stability IndicesStability Indices“Truth” LCL Retrieved LCL

First Retrieval of StabilityExample: Lifted Condensation Level (LCL)LCL may be used as a measure of boundary layer depth, and/or thedepth of the inversion atop the boundary layer (e.g., marine inversion).

HigherStability

LowerStability


PBL TurbulencePBL Turbulence

Evaluating Turbulence from Hyperspectral MeasurementsExample: Shear-Driven Instabilities: AERI-derived Boundary

Layer Depths

CBL Waves & Rolls


PBL TurbulencePBL Turbulence

June 23, 2001 autocorrelation starting 320 minutes after sunrise

August 8, 2001 e autocorrelation starting 300 minutes after sunrise

Clear Day (no clouds)

Boundary Layer Cumulus

Preliminary Findings: We appear to have identified boundary layer “roll” turbulentfeatures that produce e variations at this AERI site at periodic intervals.Ongoing work will evaluate the horizontal scales of these roll structuresbased on bulk stability parameters (e.g., Ri).

Time Difference (min) Time Difference (min)

Cor

rela

tion

Cor

rela

tion


““GVision” –GVision” –A Tool for Coalescing GIFTS-IOMI DataA Tool for Coalescing GIFTS-IOMI Data

Purpose:– Draw together disparate data for viewing atmospheric parameters

– Capitalize on in-house expertise for visualizing GIFTS-IOMI data

– Test and validate all models using within UW-MURI (NWP, RTE, etc.)

– Develop 3rd-order fields (i.e. slantwise visibility)

“VisAD” Capabilities: Java-based application, developed at UW,that is designed for the optimal manipulation and displayof large meteorological data sets.


Goal for Indian Ocean METOC ImagerGoal for Indian Ocean METOC Imager

Point Weather Data• Marine Inversion• Aerosol & dust detection• Flight-level & directional

visibility• Flight-level turbulence• SST for engine efficiency• Surface characterization

IOMI-GIFTS 4 km “Cube”


• We need to be thinking “Big”– NWP to support IOMI-GIFTS for large domains– IOMI-GIFTS data flow within an Modeling system

• NWP for next generation IOMI– IOMI-GIFTS validation experiments: NWP support – NWP to develop “turn-key” fleet-ready data system

• First Progress of Atmospheric Parameters– T, q, winds, stability, turbulence & – Continue from here ...

In Conclusion ...In Conclusion ...

workshop on hyperspectal meteorological science of uw-muri and beyond 14–15 may 2002, madison,...

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