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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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
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1.2
0 0.1 0.2 0.3 0.4 0.5 0.6
Frequency
Am
plit
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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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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.
…
…
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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
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0
1
0
1
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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
Regional Weather Information Center
Example Wind Retrieval
6 m/s 12 m/s
1122 UTC
1142 UTC
UND
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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,,,
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Regional Weather Information Center
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
Regional Weather Information Center
Example Decomposition of a Storm
Original Reflectivity Field
Small Scale Features
Medium Scale Features
Large Scale Features
UND
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
Regional Weather Information Center
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
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Regional Weather Information Center
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
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Regional Weather Information Center
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
Regional Weather Information Center
• 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