NOAA Summary Presentation

NOAA Contributions to the Central California Ozone Study and Ongoing Meteorological MonitoringJim WilczakJian-Wen Bao, Sara Michelson, Ola Persson, Laura Bianco, Irina Djalalova, and David E. WhiteNOAA/Earth Systems Research Laboratory: 

NOAA Contributions to the Central California Ozone Study and Ongoing Meteorological Monitoring Jim Wilczak Jian-Wen Bao, Sara Michelson, Ola Persson, Laura Bianco, Irina Djalalova, and David E. White NOAA/Earth Systems Research Laboratory 29 November 2006

Topics covered in this presentation (1): 

Topics covered in this presentation (1) Overview of project Model optimization ABL LSM Surface emissivity (version 3.6 vs. 3.7) Surface roughness lengths Buoy comparison Clouds and radiation Initial and boundary conditions Resolution

Topics covered in this presentation (2): 

Topics covered in this presentation (2) X x Data Assimilation Analysis nudging Observation nudging Sub-synoptic events Data denial experiments Trajectory analysis Profiler trajectory tool Model trajectories

Topics covered in this presentation (3): 

Topics covered in this presentation (3) X X X X Impact of FDDA on ozone profiles WRF simulations Profiler maintenance Seasonal modeling 15 day time series of surface met Seasonal diurnal profiler/model composites

1. Overview of project: 

1. Overview of project Project goals: Develop accurate model-based meteorological fields to be used as input to chemistry models Understand meteorology associated with high ozone events Began May 2002 Funding FY2002 ($250k) NOAA earmark FY2003 ($250k) NOAA earmark FY2004 ($250k) CCOS FY2005($250k) NOAA earmark FY2006 ($375) NOAA earmark

Slide6: 

36km grid 95x91 12km grid 91x91 4km grid 190x190 All have 50 layers, with 22 in lowest 1km and lowest model level at 12m MM5 Model Configuration

Observational Data Sets: 

Observational Data Sets Wind profiler sites

ABL schemes: 

ABL schemes Gayno-Seaman/5-layer soil MRF/5-layer soil

Slide9: 

Eta/5-layer soil

Land Surface Modules: 

Land Surface Modules Observations Eta/5-layer soil Eta/NOAH LSM

Slide11: 

Eta/NOAH LSM

Slide12: 

Eta/5-layer soil (RED) and Eta/NOAH LSM (BLUE) Temperature errors averaged for all times at 25 profiler sites 0.9 C bias

Slide13: 

Eta/5-layer soil (RED) and Eta/NOAH LSM (BLUE) wind errors averaged for all times at 25 profiler sites 0.6 m/s bias

Slide14: 

ABL Depth Evaluation

Eta/NOAH LSM combination selected : 

Eta/NOAH LSM combination selected Better phasing of diurnal variation of surface wind speed Comes closest to matching daytime max temperatures (other combinations have a larger cold bias) and Tdew Has much smaller temperature bias RMSE errors and wind speed bias above 100m than Eta/5-layer soil model However, has larger speed bias and RMSE in lowest 100m than Eta/5-layer Philosophy: Select LSM with better temperature and moisture fields, explore other factors that may reduce surface winds, let FDDA correct for larger near-surface wind errors Note: Later found that Eta/NOAH LSM wind errors with FDDA were smaller than Eta/5-layer soil model with FDDA

Surface emissivity: 

Surface emissivity

Slide17: 

Corrected emissivity improves surface temperatures, but slightly degrades surface wind RMSE

Roughness length sensitivity: 

Roughness length sensitivity MM5 specified z0 is 0.10-0.15 m in Central Valley Survey of literature of similar landscapes suggests a larger value of 0.30-0.75m. Ran numerical experiments increasing z0 by factors of 2, 5, and 10

Slide19: 

Optimal z0 is about 5x larger, in agreement with literature values

Buoy comparison: 

Buoy comparison

Slide21: 

Buoy comparison z0 over ocean looks OK

Clouds and radiation: 

Clouds and radiation Compare satellite visible imagery with model integrated cloud liquid water Two distinct cloud types are present: low-level coastal stratus and upper-level clouds over land

Slide23: 

Non-FDDA FDDA 1800 UTC 29 July

Slide24: 

Non-FDDA FDDA 1800 UTC 30 July

Slide25: 

1800 UTC 31 July Non-FDDA FDDA

Slide26: 

1800 UTC 1 Aug Non-FDDA FDDA

Slide27: 

Non-FDDA FDDA

Slide28: 

Differences between observed and simulated solar radiation are within the error bars of the observations.

Clouds and radiation summary: 

Clouds and radiation summary MM5 replicates patchy, intermittent coastal stratus MM5 also produces intermittent high-level clouds over land Timing and locations of clouds are not always correct, but cloud statistics appear ok FDDA can alter cloud fields, sometimes for the better, sometimes for worse MM5 solar radiation agrees with observations within the observational error

Initial and Boundary Conditions: 

Initial and Boundary Conditions NCEP 40km Eta analysis (AWIPS) European Centre’s 0.5 deg (~50 km) analysis (ECMWF)

Slide31: 

AWIPS ECMWF 850 mb temperatures (color shaded), geopotential heights (solid black contours) and winds at 1200 UTC 29 July 2000 from the AWIP and ECMWF analyses on the 36-km grid

Slide32: 

AWIPS ECMWF

Initial and Boundary Conditions Summary: 

Initial and Boundary Conditions Summary Significant wind differences exist between the AWIPS and ECMWF simulations at any given time and height However, statistically one is not significantly better than the other ECMWF produces a larger surface cold bias

Horizontal grid resolution (1.33 vs. 4 km): 

Horizontal grid resolution (1.33 vs. 4 km) Average over all profiler sites except GLA

High resolution: 

High resolution 1.33km resolution slightly improved the surface winds, reducing the high wind speed bias Higher resolution reduced nighttime cold bias, but also increased daytime cold bias by a smaller amount At some sites higher resolution led to more significant improvements

Four Dimensional Data Assimilation (FDDA): 

Four Dimensional Data Assimilation (FDDA) FDDA applies a correction term to the model equations at each time step that brings the model variables closer to the observed values The size of the correction term is proportional to the difference between the model variable and the observation If the model is already in reasonable agreement with the observations, the correction term is small, and the model remains in near dynamical balance

Slide37: 

Analysis (grid) nudging was applied on the 36 km grid using the time-interpolated 6-hourly AWIPS analyses. Winds, temperatures, and moisture were assimilated at heights above the model-diagnosed ABL height. Obs nudging was done for profiler and surface winds, using a 50 km e-folding radius of influence.

Slide38: 

Observed winds Non-FDDA simulation Arbuckle winds on 30 July 2000

Slide39: 

Observed winds FDDA simulation Arbuckle winds on 30 July 2000

Slide40: 

Vector wind difference at Arbuckle on 30 July

Slide41: 

Averages over 25 wind profiler sites and all times

Slide42: 

Averages over 25 wind profiler/RASS sites and all times

Slide43: 

FDDA makes simulated and observed wind data almost indistinguishable from one another FDDA also significantly improves temperature bias and RMSE How far does influence of obs nudging extend away from profiler sites? Are their times when FDDA does not work well?

Data Denial ExperimentFDDA at all sites except CCO, SAC, SVS, AGO: 

Data Denial Experiment FDDA at all sites except CCO, SAC, SVS, AGO Wind statistics averaged at 4 profiler sites (CCO, SAC, SVS, AGO)

Slide45: 

Temperature statistics averaged at 4 profiler sites (CCO, SAC, SVS, AGO)

Slide46: 

Effective radius of influence RMSE for three simulations, MFDi, MFDiwh6, and MNFD Re(winds) ~ 50 km Re(temp) ~ 260km

Sub-synoptic events: 

Sub-synoptic events Observed winds Non-FDDA Winds at Lemore on 1 August 2000

Slide48: 

Observed winds FDDA winds Winds at Lemore on 1 August 2000

Slide49: 

24-h forward model trajectories for parcels released at Sacramento At 00 UTC 31 July 2000. Red is for a release from the lowest model level, Blue 100m, and black 500m. Non-FDDA FDDA Trajectory Analysis

Trajectory Analysis: 

Trajectory Analysis Wind profiler trajectory analysis tool Trajectory Analysis

Slide51: 

FDDA Non-FDDA

Trajectory Analysis: 

Trajectory Analysis FDDA can make a significant difference in trajectory paths (trajectories are very sensitive to small changes in the winds) If attribution of specific ozone events is desired, FDDA is essential Purely observational and model trajectories can be calculated and compared

Effect of FDDA on vertical ozone profiles: 

Effect of FDDA on vertical ozone profiles Ozone statistics from 16 soundings taken at Granite Bay and Parlier. Granite Bay: bias/no change; RMSE/improved; correlation/improved Parlier: bias/degraded; RMSE/no change; correlation/improve

WRF/MM5 Comparisons: 

WRF/MM5 Comparisons 2-m temperature averaged over the southern SJV WRF is slightly warmer than MM5

Slide55: 

10-m wind speed averaged over the southern SJV WRF also has a high wind speed bias

WRF summary: 

WRF summary Relatively small differences were found between WRF and MM5 NOAA’s WRF simulations were provided to BAAQD and run through CAMx, providing providing ozone simulations that were statistically equivalent to MM5 WRF is ready for California AQ studies

Profiler Maintenance: 

Profiler Maintenance NOAA maintained three profilers, at Chico, Chowchilla, and Lost Hills NOAA returned the Bay Area’s Livermore profiler to service Purchased a new system computer Modified the radar controller card and coherent integrator card to make them compatible with the revised PCI standard of the new computer system Replaced the DSP card with double the previous memory, which can allow for additional range gates Checked all antennas and switches Replaced one RASS voice coil that was not working Installed software that allows NOAA engineers to remotely monitor the health of the profiler system, and to remotely upload modifications to the profiler operating system Initiated the real-time display of the Livermore data on NOAA’s profiler web site.

Seasonal Modeling: 

Seasonal Modeling QC’d 25x122=3050 profiler days of winds and RASS Provided 3050 days of ABL depths Ran 122 days of MM5 simulations (non-FDDA) Created seasonal averaged, diurnal time-height cross-sections at each profiler site

Slide59: 

15-day time series of surface met at Bakersfield

Slide60: 

60% of observed winds required to plot a vector 30% for observed ABL depths Richmond

Slide61: 

Grass Valley

Slide62: 

Redding

Slide63: 

Arbuckle

Slide64: 

Los Banos

Slide65: 

San Joaquin Valley (Visalia)

Slide66: 

Bakersfield

Seasonal Modeling Summary: 

Seasonal Modeling Summary Non-FDDA model replicates predominant “climatological” flow patterns at each profiler site Flow features reproduced include: bifurcation of flow in the delta region Nocturnal jet in San Joaquin Valley Fresno and Schultz eddies Timing of upslope/downslope along the Sierras ABL depth magnitude and spatial variation Biggest shortcoming is that model underestimates southerly flow along eastern side of Sacramento Valley from SAC to RDG