Work Plan. Air Quality Research Program (AQRP) Project 12-TN1

Transcription

1 Work Plan Air Quality Research Program (AQRP) Project 12-TN1 Investigation of surface layer parameterization of the WRF model and its impact on the observed nocturnal wind speed bias: Period of investigation focuses on the Second Texas Air Quality Study (TexAQS II) in 2006 Originally prepared on October ; final revision on January 28, 2013 Principal Investigator: Daniel Tong, University of Maryland Purpose This project examines the various similarity theories that parameterize the momentum fluxes of the surface layer used in the Weather Research and Forecasting (WRF) meteorological model. A study of the underlying field data employed to derive the coefficients and the tunable constants of the parameterizations will be performed to hypothesize why the WRF application over-predicts the lowlevel nocturnal wind speeds. Temporal variability of exchange coefficients for moisture, heat, and momentum and associated fluxes during the early evening hours from the similarity parameterization used in the YSU PBL scheme will be extracted from the model for detailed examination. Analysis of these variables as functions of atmospheric stability, proximity to the coastline, and the strength of the land and sea breeze that prevailed earlier in the day will be examined to understand the model deficiency. Furthermore, by adjusting some of the tunable constants the over-predictions may be reduced. The project has the following specific objectives: 1. To conduct a literature search to summarize the sensitivity of each version of the similarity theories used by the WRF model and their corresponding underlying field data. 2. To modify WRF to extract exchange coefficients for moisture, heat, and momentum and associated fluxes from the similarity parameterization used in the YSU PBL scheme. 3. To analyze the variability of the extracted variables as a function of atmospheric stability, time of day, and surface moisture. 4. To examine and evaluate the role of surface layer similarity theories as a closure scheme on the nocturnal wind speed bias. 5. To draft a final report that documents all work performed in support of the project. 6

2 Background This study investigates surface layer parameterizations in the WRF model. The parameterization of energy fluxes from the surface layer significantly impacts the modeled near-surface winds. Several recent studies of the meteorological features of the regions adjacent to the Gulf of Mexico using the WRF model have identified a frequent nocturnal wind speed over-prediction (e.g., Byun et al., 2008, Lee et al., 2010). These wind speed high biases can cause loss of accuracy in the simulation of advection of pollutant precursors on days with elevated surface ozone concentrations. The persistent overprediction of nocturnal wind speeds by the model displaces precursor and oxygenated chemical species that participate in photochemical reactions augmented at sunrise. Consequently, the concentrations of these species are initialized incorrectly in the air quality simulation that depends on WRF predicted meteorology, leading to significant error in the prediction of the daily maximum ozone concentrations that typically occurs in the afternoon. The WRF model tends to over-predict the surface wind speed in eastern Texas in the evening hours, especially in coastal regions such as the Houston-Galveston- Brazoria (HGB) area. This wind bias prevails more noticeably when there is a high pressure system centered over the Louisiana/Mississippi/Arkansas area that is associated with easterly/southeasterly flow in the lowest hundreds of meters in Southeastern Texas (Ngan et al., 2013). This project builds on the findings from an earlier NOAA project and further examines the incorrect redistribution of kinetic energy from the night time residual layer to the surface (Lee et al., 2012). Focus on the surface layer similarity theory The WRF meteorological model uses surface layer similarity theory to describe flux-profile relationships for heat, moisture and momentum. For instance, a non-dimensional profile defined for momentum prescribed by the similarity theory takes a form as follows: kz U u z * m z L Where k is von Kármán s constant, z is altitude, u * is friction velocity, U is mean wind speed, m is profile function for momentum derived empirically from field data, L is the Monin-Obukhov length that can be expressed as: 2 T0u* L gk * WhereT 0 is averaged temperature in the surface layer, g is gravitational acceleration, and * is surface temperature scale factor defined in terms of kinematic flux of heat ( F h ), * F h u*. The coupling of the surface layer parameterization and the surface turbulence mixing scheme is the focus of this study. We choose the same period and region in the HGB area leveraged from a previous TCEQ-funded project for our current study (Lee et al., 2012). Our objective is to identify possible tunable constants that can be 7

3 adjusted to reduce the wind speed biases of the low-level nocturnal wind speeds. During an extended 37 day episode between May 28 th and July 3 rd 2006, a significant over-prediction of wind speeds occurred during June During that period, the synoptic weather charts showed a high pressure system regionally centered over Louisiana, Mississippi, and Arkansas; the clockwise circulation around this ridge of high pressure was associated with easterly/southeasterly winds in the lower troposphere in southeastern Texas. This weather condition was favorable to the development of a sea breeze that had southerly to southwesterly flow near the surface by the afternoon. Analysis of upper-level winds showed that WRF captures the forward turning of wind vectors (e.g. clockwise in the northern hemisphere) during the evening hours consistent with an inertial oscillation in the Low Level Jet (LLJ) at heights around 350 m. On the other hand, WRF did not capture the backward oscillation defined by the displacement from equilibrium (e.g., Van de Wiel et al., 2010) that should have retarded wind speeds in the lowest levels. These findings indicated that the modeled nocturnal bias in wind speeds is a product of particular synoptic patterns and appears to be associated with inaccurate momentum fluxes in the lowest levels. The WRF model treats the surface layer -- typically tens of meters in thickness, explicitly using similarity theory. It calculates friction velocities and exchange coefficients between the surface and lowest model layer. Similarity theory provides a means to compute surface exchange coefficients for heat, moisture, and momentum. The WRF model parameterizes these quantities using one of three types of similarity theory: (1) stability functions dating to work by Paulson (1970) compounded with a convective velocity enhancement by Beljaars (1994); (2) a surface layer scheme developed for the Eta model using the Monin Obukhov length scale (Janjic 2002); and (3) the Pleim-Xu (PX) surface layer scheme (2006). The implementation of the theory takes a semi-empirical approach derived from field experiments. A key assumption is that a particular field experiment can generate universal flux profile functions which are suitable for other locations, especially if atmospheric conditions are similar. Each planetary boundary layer (PBL) parameterization in WRF has a recommended surface layer parameterization. Monin-Obukov similarity theory is coupled with the YSU PBL scheme; the Eta surface layer scheme works with the Mellor-Yamada-Janjic (MYJ) PBL scheme; and the PX surface layer scheme works with the Asymmetric Convective Model Version 2 (ACM2) PBL scheme. The investigation and possible remedy recommendation for rectifying the high wind-speed-bias will be carried out in multiple phases: (A) Understand the sensitivity of the different surface layer schemes, (B) Examine the sensitivity of the flux-profile relationships with regards to synoptic and stability meteorological conditions, and (C) If the above two steps warrant further investigation of the surface layer schemes, examine the universal flux profile functions (e.g. Foken 2006) and the range of parameter values used by the functions to suggest potential modifications for improvement especially for the stable regime. This is important as the correct timing of the decoupling of near-surface and surface phenomenon is critical in the redistribution of kinetic energy from the residual layer to the surface. This transfer of energy governs the wind speeds in the lowest layers. 8

4 PROJECT TASKS This project is composed of 8 tasks. A description of each task, including schedule for completion, is provided below. The project schedule is also summarized in Table 1. Tasks 1 and 2: Literature Review and summary of surface layer schemes in WRF We will examine the various similarity theories that parameterize the momentum fluxes of the surface layer in the WRF meteorological model. A study of the underlying field data employed to derive the coefficients and the tunable constants of the parameterizations will be performed to hypothesize why the WRF application over-predicts the low-level nocturnal wind speeds. Responsible Party: Daniel Tong, Fantine Ngan and Pius Lee; Due date: April Task 3: Modify WRF to output surface flux variables In order to analyze the surface layer similarity theory parameterizations, definition of additional global variables is necessary. Some of these new variables are intermediate values in the flux calculations by the theory. Multiple levels of subroutines in the model may be required to be modified to output these intermediate variables pertinent to specific processes of the momentum flux in the surface layer. A description of the code modifications and compilation of time series for the most impactful parameters governing the surface fluxes will be provided in the September 2013 monthly technical report. Responsible Party: Daniel Tong, Li Pan and Fantine Ngan; Due Date: August Task 4: Prepare and process model verification data Model evaluation will focus on simulation results from the 4 km horizontal resolution domain covering Eastern TX. There are 46 Continuous Air Monitoring Sites (CAMS) within the domain. In addition, within the HGB region, a wind profiler operated by the Cooperative Agency Profilers is available at La Porte. It is paired with CAMS site C35. Furthermore, University of Houston Coastal Center (UHCC) operated by the University of Houston provides measurement for meteorological parameters including temperature, wind, precipitation and fluxes. These observational data will be processed and quality assured for model verification. Responsible Party: Daniel Tong and Fantine Ngan; Due Date: August

5 Task 5: Analyze time series of the flux variables We will extract exchange coefficients for moisture, heat, and momentum and their associated fluxes from the similarity parameterization used by the YSU PBL scheme. We will also analyze the variability of the extracted variables as a function of atmospheric stability, time of day, and surface moisture. Responsible Party: Daniel Tong, Li Pan and Fantine Ngan; Due Date: October Task 6: Recommend adjustment of tunable constants The study will include time series plots for bias and root mean square error for wind speeds for the most impactful alternations of the tunable parameters. Basing on rankings of these most impactful alternations, recommended values of these tunable parameters will be reported in the October Monthly Progress Report. Responsible Party: Daniel Tong, Fantine Ngan and Pius Lee; Due Date: September Task 7: Conduct WRF sensitivity runs A series of sensitivity runs of the WRF model will be devised and conducted with possible recommended adjusted values for several of the tunable constants in the surface layer similarity theory parameterization. This task is conducted in concert with Task 6. Although the runs will focus on an early summer period for the HGB area, they should provide insight on the rate and strength of coupling and decoupling between the surface layer and the lowest model level turbulent mixing in a large range of land-use and meteorological conditions. Result of the sensitivity runs and their verification statistical rankings will be tabulated and interpreted. Responsible Party: Daniel Tong, Fantine Ngan, Li Pan and Pius Lee; Due Date: October Task 8: Reporting Monthly technical reports will be submitted by the 8 th day of each month with an accompanying financial report submitted by the 12 th day of each month throughout the duration of the project. A final technical report will be submitted by November 30, 2013, preceded by a draft final report on October 21, Other reports (e.g., Executive Summary, Quarterlies) will be submitted as requested by AQRP. Responsible Party: Daniel Tong and Pius Lee; Due Date: November

6 Table 1. Schedule of project activities. ID Task 2/13 3/13 4/13 5/13 6/13 8/13 9/13 10/13 11/13 1 Literature Review X X X 2 Summarize surface layer schemes in WRF 3 Modify WRF to output surface flux variables X X X X X X 4 Prepare and process model verification data 5 Analyze time series of the flux variables 6 Recommend adjustment of tunable constants 7 Conduct WRF sensitivity runs X X X X X X X X X X X X X X 8 Reporting X X X X X X X X X 11

7 Texas Air Quality Research Program Project Manager Gary McGaughey Principal Investigator Daniel Tong Institution : Cooperative Institute for Climate and Satellites, University of Maryland Department : Department of Atmospheric and Oceanic Science Address : 5830 University Research Court, Suite 4163, College Park, Maryland phone : fax : Daniel.Tong@noaa.gov REFERENCES Beljaars, A. C. M., 1994: The parameterization of surface fluxes in large-scale models under free convection, Quart. J. Roy. Meteor. Soc., 121, Byun, D. W., F. Ngan, X. S. Li, D. G. Lee, S. T. Kim and H. C. Kim, 2008: Evaluation of retrospective MM5 and CMAQ simulation of TexAQS-II Period with CAMS measurements, Final Report for Texas Commission on Environmental Quality, February 2008, 30pp. Byun, D. W. and K. L. Schere, 2006: Review of the governing equations, computational algorithm, and others components of the Models-3 Community Air Quality (CMAQ) modeling system. Applied Mechanics Reviews, 59, Dudhia, J., 1989: Numerical Study of Convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, Foken, T., 2006: 50 years of the Monin-Obukhov similarity theory. Bound.-Layer Meteor., 119, Grell, G.A., Dudhia, J., Stauffer, D., A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), NCAR Technical Note: NCAR/TN-398þSTR. 12

8 Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation, Mon. Wea. Rev., 132, Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF Single-Moment 6-Class Microphysics Scheme (WSM6), J. Korean Meteor. Soc., 42, Hong, S.-Y., and Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, Janjic, Z. I., 2002: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note, NO. 437, 61pp. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, Lee, P., F. Ngan and H. C. Kim, 2012: Investigation of nocturnal surface wind bias by the Weather Research and Forecasting (WRF)/ Advanced Research WRF (ARW) meteorological model for the Second Texas Air Quality Study (TexAQS-II) in 2006, Final Report for Texas Commission on Environmental Quality, March 2012, 26pp. Lee, S. H., S. W. Kim, W. M. Angevine, L. Bianco, S. A. McKeen, C. J. Senff, M. Trainer, S. C. Tucker, and R. J. Zamora, 2010: Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign, Atmos. Chem. Phys., 11, Michalakes, J., J. Dudhia, D. Gill, T. Henderson, J. Klemp, W. Skamarock, and W. Wang, 2004: "The Weather Reseach and Forecast Model: Software Architecture and Performance,"to appear in proceedings of the 11 th ECMWF Workshop on the Use of High Performance Computing In Meteorology, October 2004, Reading U.K. Ed. George Mozdzynski. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RTTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102, Ngan, F., H. Kim, P. Lee, K. Al-Wali and B. Dornblaser, 2013: A study on nocturnal surface wind speed over-prediction by WRF-ARW Model in Southern Texas, to be submitted to J. appl. Meteor., under review. Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. appl. Meteor., 9, Pleim, J. E., 2006: A simple, efficient solution of flux-profile relationships in the atmospheric surface layer. J. Appl. Meteor. and Climatology, 45,

9 Van de Wiel, B. J. H., A. F. Moene, G. J. Steeneveld P. Baas, F. C. Bosveld, and A. A. M. Holtslag, 2010: A conceptual view on inertial oscillations and nocturnal low-level jets. J. Atmos. Sci., 67,