Chemical Biological Applications of Mesoscale Atmospheric Modeling Presenter: John Mewes Department...

Chemical Biological Applications of Mesoscale

Atmospheric Modeling

Presenter:John Mewes

Department of Atmospheric SciencesUniversity of North Dakota

Research Team:Leon Osborne (UND)John Mewes (UND)Paul Kucera (UND)

Mark Askelson (UND)Ben Podoll (GRA UND)

Todd Williams (GRA UND)Rhesa Freeman (URA UND)

Kaycee Frederick (URA UND)

UND

Regional Weather Information Center

Outline

Objective Analysis (M. Askelson)

Wind Motion using a Cross-Correlation Analysis Technique (P. Kucera)

Mesoscale Data Assimilation for Model Initialization (L. Osborne)

Land-Surface Modeling efforts at the University of North Dakota (J. Mewes)

Objective Analysis Technique

Mark A. AskelsonUniversity of North Dakota

AHPCRC Annual Review MeetingAugust 2003

UND


Background Challenges

Irregularly-distributed data have deleterious effects on the efficacy of analysis schemes

Utility of mesoscale NWP forecasts Depends on accuracy (predictive and scale)

Depends on initialization errors (amounts, variables, etc.) Need to explore sensitivity to realistic changes in model initialization

variables (land-use, resolution, humidity, etc).

UND


Background

Purpose Alleviate deleterious effects of irregular data

distribution by incorporating the response filter into LAPS.

Example of Analyses, Amplitude and Phase Modulations for Three Different Analysis Schemes.

UND


Background

Run atmospheric models at very high resolution to explore utility in supporting Army operations

Evaluate model sensitivity to model parameters expected to cause significant differences in small-scale fields

Resolution Land-use Physics parameterizations

Simulated rain-water, cloud-water, and surface flow fields (MM5)

UND


ResultsResults Response filter partially incorporated into LAPS

Development and initial testing of 1D and 2D filters complete. Identified LAPS routines that use empirical weighting techniques. Identified LAPS routines that generate empirical weights. Designed interfaces for response filter.

Desired Amplitude Responses vs. Amplitude Response Modulations When The Reference Number is 4

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6

Frequency

Am

plit

ud

e R

es

po

ns

e

Desired Amplitude Responses

Amplitude Response Modulations

Figure that shows that the 1D response filter can reproduce

the desired amplitude modulation when data are

irregularly distributed.

UND


Results Tests of model sensitivity

Summer Institute Crystal Paulsen (UND) and Georgette Holmes (JSU) Experiments run on the Cray X1 Lots of help from Tony Meys (NetASPx)

Hor. grid spacing: 20, 10, 5 and 1 km over large domain.

Q, dx = 20 km, z = 1.6 km Q, dx = 1 km, z = 1.6 km

UND


ResultsLand-Use

Changed area of crop land to bare ground in north central Oklahoma (apparent as red box in bottom-right image).

Cloudiness over area changed.

Clouds and surface T, original land use (crop land)

Clouds and surface T, changed land use (bare ground)

UND


Conclusion

Results Response filter appears to be superior to ‘simple’ schemes. MM5 shows significant sensitivities to grid spacing, land

use, and physics parameterizations (Students’ presentation at http://www.ahpcrc.org/~cpaulsen/index.html)

Future Work Response filter and LAPS

Finish integration Real-time testing

Model sensitivity testsCompare with observationsPerform more tests (e.g., dx = 0.5, 0.25 km)

Wind Motion using a Cross-Correlation

Analysis Technique

Paul A. KuceraUniversity of North Dakota

AHPCRC Annual Review MeetingAugust 2003

UND


Cross-Correlation Analysis (CCA) of Lower Tropospheric Wind Fields

Motivation: Provides information about the 3-D wind fields in

regions with very few or no direct observations (i.e. rawindsondes)

Provides spatial wind estimates for improved mesoscale model initialization in data sparse regions

Issues: Assumes cloud and precipitation elements are quasi-

steady-state between each time interval Sensitive to spatial and temporal resolution of the

data CCA is computationally intensive that is well-suited for

high-performance computing

UND


CCA Technique

Determine Lagrangian Autocorrelation for horizontal lags α, β between images at time, t, and t + τ. The parameters S and T are the observations in search window and surrounding target windows, respectively, and n, m are the dimensions of the windows.

…

…

…

Second Image OverlaidWith Cartesian Grid

First Image OverlaidWith Cartesian Grid

Search Area AroundCorresponding GridPoint in Second Image

Search Location AtCenter of Grid PointIn First Image

1

0

1

0

1

0

1

0

22

1

0

1

0,, n

l

m

k

n

l

m

kklklklkl

n

l

m

kklklklkl

TTSS

TTSSR

The location of maximum correlation for lags, α, β at time lag, τ will determine “best” direction and speed of the elements in search window, S

UND


Example Wind Retrieval

6 m/s 12 m/s

1122 UTC

1142 UTC

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Verification of CCA Technique

2 km Altitude

Rawindsonde – 0933 UTC

12 m/s

CCA Technique: RMSE ~15 deg in wind direction ~ factor 2 underestimation in wind speed

UND


Error Analysis

6 m/s 12 m/s

6 m/s 12 m/s 6 m/s 12 m/s

0942 UTC

0952 UTC 1152 UTC

Large errors due to temporal evolution of the storms between time steps

UND


Improved Approach: Echo Tracking Combined with Spatial Decomposition Retrievals

Currently implementing echo tracking (CCA Technique) along with spatial decomposition algorithms developed by the BMRC Australia for nowcasting of severe storms (Seed 2003)The algorithm is computationally efficient and has the ability to reduce retrieval error significantly (~50% reduction in RMSE) through the decomposition of various storm scales.Spatial Decomposition Algorithm: assumes that storms have a multiplicative structure that are organized

as continuum of scales ranging from 100 m to 100’s of km Storm structure can be decomposed using a FFT and a bandpass filter

centered each cascade scale based on the following equation:

where Xk,i,j is the field of the scale k, L is the spatial domain size for each scale k

nn

kjikji LjjLitXtF 2;,...,1;,...1;

1,,,

UND


Spatial Decomposition of Storm Scales

The storm structure at different scales can be characterized by its autocorrelation functionThe lifetime of a pattern of the reflectivity field is dependant on its scale (i.e. small scales are less correlated)Use a Autoregressive model order 2, AR(2), to predict the evolution of the storm at various scales using the equation

Where Φk,1(t) and Φk,2(t) are the model coefficients using the Yule-Walker equations

Example Autocorrelation functions 12,1 tXtΦtXtΦτtX k,i,jkk,i,jk,k,i,j

UND


Example Decomposition of a Storm

Original Reflectivity Field

Small Scale Features

Medium Scale Features

Large Scale Features

UND


Current Research Activity

Currently implementing the echo tracking/spatial decomposition software to large WSR-88D radar dataset:

4 months (July-October 2002) WSR-88D radar data from South Florida (Key West, Miami, Melbourne, and Tampa Bay).

9500 merged radar maps at a 6-min temporal and 2 km x 2 km horizontal 1 km vertical resolution (1 km – 12 km altitude)

Spatial domain: 900 km x 900 km Data have been QC’ed by students

Near Term: Develop an interface to ingest wind fields

into LAPS Parallelize code for implementation on

the AHPCRC computer resources

Mesoscale Data Assimilation for Model

Initialization

Leon F. Osborne, Jr.Director, Regional Weather Information

Center

Professor, Atmospheric Sciences

University of North Dakota

Grand Forks, North Dakota

UND


Challenges and Relevance of Investigation

Work focuses on enhancing detail of boundary layer structure in an operational data assimilation system (LAPS) Improving data acquisition of low-atmosphere data Establishing multiple analysis layers within atmospheric

boundary layer

Challenges Lack of direct PBL observations Expanding LAPS code to accommodate

expanded remotely sensed wind observations

Relevance Provide improved initialization of mesoscale and

CFD models yielding improved chemical-biological dispersion forecasts

UND


Core Analysis Method

J = JB + JO + JC

JB is a weighted fit of the analysis to the background field

JO is a weighted fit of the analysis to the observations

JC is a term which can be used to minimize the noise produced by the analysis (e.g., by introducing a balance).

3D-Variational adjustment is applied to objectively analyzed fields containing heterogeneous data types:

UND


Three-Dimensional Variational Assimilation

Domain initialized with a previous forecast for mass, momentum and moisture

Utilizes data models i.e. Doppler radial winds in data assimilation

3DVar adjustments are made throughout the atmosphere including new data layers in the PBL

Transformation matrix, K,is replaced by models

for various remotely observed data

2, ,,

,

2 2 2

, , , , , ,

2

, ,

2 2 2

, , , , , ,

1

2

1

2

1

2

1

2

O B D S

m n m nO m n r rob

m n

B ub b vb b wb bi j k i j k i j k

D Di j k

S us vs wsi j k i j k i j k

J J J J J

J CV V

J u u v v w w

J D

J u v w

UND


Data Assimilation ActivitiesObserved Data Sources

In Situ METAR, SYNOP, Mesonet, Aircraft, Rawinsonde

Remote Sensing GOES, POES, NEXRAD

Model Backgrounds Meso-ETA

Provides background field for observation refinement

Data Volume (all domains) Input: 425 Mbytes each hour Output: 1,439 Mbytes each hour

Frequency Hourly across 3 domains

Grid Spacing: Vertical: 35-40 levels (maximized for PBL support) Horizontal: 5-kilometers

UND


UND LAPS Data Assimilation SupportProvides primary data support for UND AHPCRC atmospheric science research activities

Prepares a 3-Dimensional representation of atmospheric structure and conditions

Includes parameterizations for depicting the presence of clouds within moisture fields

Initialization data for MM5 and WRF mesoscale modeling

Provides cold-start initialization as default Hourly data assimilation provides inputs for

FDDA initializations (warm-start) Supports diabatic initialization (hot-start) for

MM5 and WRF with proper adjustments to mesoscale model initialization codes

UND


Accomplishments:

Modification of LAPS code to support non-uniform vertical levels Expansion of LAPS levels within atmospheric boundary layer to provide 10 hPa resolutions

Incorporated multiple Doppler radar into LAPS momentum analysis routines using CRAFT provided data

real-time data processing of WSR 88-D Level II data to produce 3-D volumes of cloud and wind information

Boundary layer enhancements to LAPS to permit

Development of an interactive LAPS profile retrieval system and interactive LAPS display capability for researchers (next slide)

UND


Interactive retrieval of LAPS dataInteractive retrieval of LAPS data

Permits researchers to download location specific data regions and profiles for use in model testing.

Interactive LAPS data viewerInteractive LAPS data viewer

LAPS does not have an inherent visualization toolkit as released by FSL. A java-based visualization system has been developed at UND that permits researchers to selectively view 2-D and 3-D datasets. A web-based applet has been developed for offsite users.

AHPCRC Annual Review Meeting AHPCRC Annual Review Meeting

August 27August 27thth, 2003, 2003

AHPCRC Chem-BioAHPCRC Chem-Bio

LAND-SURFACE LAND-SURFACE MODELINGMODELING

efforts at the University of North efforts at the University of North DakotaDakota

Dr. John J. MewesDr. John J. MewesAssociate ProfessorAssociate Professor

Atmospheric SciencesAtmospheric Sciences

UND


GoalGoal

To improve analyses and short-term forecasts of the lower atmospheric stability structure by coupling an advanced Land Surface Model (LSM) to the Local Analysis and Prediction System (LAPS)

WhyWhy

The stability structure of the lower atmosphere is of primary importance in modulating both its dispersive properties and the effects it has on the propagation of electromagnetic radiation.

Critically ImportantCritically Important• Latent heat fluxes

• Sensible heat fluxes

• Emission, absorption and reflection of radiation

UND


Chose the “NOAH” Land-Surface Model because of its present sophistication and potential for further enhancements by the LSM community.

• Embedded the NOAH LSM within the LAPS framework, using LAPS analyses of temperature, winds, humidity, cloud cover (to calculate radiation), and precipitation as forcing.

• Added a ‘tiling’ feature to instill the effects of sub-grid scale land surface variations into the atmospheric analyses.

HowHow

UND


Basic idea is that the fluxes over one grid cell are a weighted aggregation of the fluxes from each ‘tile’ of unique soil / vegetation type pairing within the cell:

Fcell=F1A1+F2A2+….+FNAN

where each cell (1..N) has a unique pairing of soil and vegetation characteristics and an area (A) that is representative of their actual distribution within the cell.

TilingTiling

UND


D.C. / Baltimore Corridor

WichitaTulsaOklahoma City

LSM is operational and undergoing operational testing in several domains.

• Primary verification efforts are being conducted in the Southern Plains to take advantage of vast ARM & Oklahoma Mesonet observational resources.

Current StatusCurrent Status

UND


• Continue LSM verification, tuning, and enhancement efforts.

• Begin utilizing the LAPS LSM heat and radiative fluxes to improve LAPS analyses of the lower atmospheric stability structure.

• Parameterize stability structure in terms of fluxes and ambient atmospheric characteristics?

• Drive a 1-D PBL model?

• Use LSM fields to initialize a short-term mesoscale model (that also uses NOAH) forecast, which can then serve as the background field for the next analysis?

• Other possibilities?

Immediate Research PlansImmediate Research Plans

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