Chemical Biological Applications of Mesoscale Atmospheric Modeling

1. Chemical Biological Applications of Mesoscale Atmospheric Modeling Presenter: John Mewes Department of Atmospheric Sciences University 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)

2. 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)

3. Objective Analysis Technique Mark A. Askelson University of North Dakota AHPCRC Annual Review Meeting August 2003

4. 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).

5. 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.

6. 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)

7. Results • Results • 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. Figure that shows that the 1D response filter can reproduce the desired amplitude modulation when data are irregularly distributed.

8. 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

9. Results • Land-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)

10. 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 tests • Compare with observations • Perform more tests (e.g., dx = 0.5, 0.25 km)

11. Wind Motion using a Cross-Correlation Analysis Technique Paul A. Kucera University of North Dakota AHPCRC Annual Review Meeting August 2003

12. 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

13. 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. 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

14. 6 m/s 12 m/s Example Wind Retrieval 1122 UTC 1142 UTC

15. Rawindsonde – 0933 UTC 2 km Altitude 12 m/s Verification of CCA Technique CCA Technique: RMSE ~15 deg in wind direction ~ factor 2 underestimation in wind speed

16. 6 m/s 6 m/s 6 m/s 12 m/s 12 m/s 12 m/s Error Analysis 0942 UTC Large errors due to temporal evolution of the storms between time steps 1152 UTC 0952 UTC

17. 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

18. Spatial Decomposition of Storm Scales • The storm structure at different scales can be characterized by its autocorrelation function • The 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)andΦk,2(t)are the model coefficients using the Yule-Walker equations Example Autocorrelation functions

19. Example Decomposition of a Storm Large Scale Features Original Reflectivity Field Small Scale Features Medium Scale Features

20. 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

21. 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

22. 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

23. Core Analysis Method 3D-Variational adjustment is applied to objectively analyzed fields containing heterogeneous data types: 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).

24. 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

25. Data Assimilation Activities • Observed 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

26. UND LAPS Data Assimilation Support • Provides 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

27. 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)

28. Interactive retrieval of LAPS data Permits researchers to download location specific data regions and profiles for use in model testing. Interactive 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.

29. AHPCRC Chem-Bio LAND-SURFACE MODELING efforts at the University of North Dakota Dr. John J. Mewes Associate Professor Atmospheric Sciences AHPCRC Annual Review Meeting August 27th, 2003

30. Goal 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) Why 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 Important • Latent heat fluxes • Sensible heat fluxes • Emission, absorption and reflection of radiation

31. How • 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.

32. Tiling 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.

33. D.C. / Baltimore Corridor Wichita Tulsa Oklahoma City Current Status 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.

34. Immediate Research Plans • 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?