A Performance Evaluation of Lightning-NO Algorithms in CMAQ

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Presentation transcript:

A Performance Evaluation of Lightning-NO Algorithms in CMAQ Daiwen Kang1, David Wong1, Kristen Foley1, George Pouliot1, Wyat Appel1, Shawn Roselle1, and Pius Lee2 Computational Exposure Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park Air Resources Laboratory (ARL), NOAA, College Park, MD 16th Annual CMAS Conference Chapel Hill, North Carolina, USA 23-25 October, 2017

Lightning NO Production Schemes in CMAQ An observation-based parameterization scheme (mNLDN): Use monthly mean NLDN (National Lightning Detection Network) data to constrain the lightning strikes over the domain Distribute the total lightning strikes to the local grid cells by relating NLDN strikes to grid-level Rain Convective (RC) values (CMAQ5.1 and earlier,) A log-linear regression parameterization scheme (pNLDN) based on multiyear NLDN data and met model predicted convective rainfall using Kain-Fritsch (KF) convective scheme (CMAQ5.2). A lightning NO scheme (hNLDN) directly based on hourly NLDN data (gridded lightning strikes) (CMAQ5.2).

Model configuration and simulations Met Inputs: WFR 3.8 with lightning assimilation (Heath et al., 2016) CTM: CMAQ5.2beta Model Domain: 12 km contiguous US with 35 vertical layers Emissions: 2011 NEI (National Emissions Inventory), NH3 bidi, inline biogenic emissions Simulation Period: April – September, 2011, with 10-day spin-up Model cases: Base (No lighting NO production), mNLDN (monthly NLDN based scheme), pNLDN (linear (log-linear) regression based scheme), and hNLDN (hourly NLDN based scheme). Observations: AQS (Air Quality System) and NADP (National Atmospheric Deposition Program) /NTN (National Trend Network)

Sum of Monthly and Daily Domain Column LTNG NO Produced by the CMAQ schemes hNLDN mNLDN pNLDN hNLDN mNLDN pNLDN July, 2011 2011 Due to the fact that the RC field was unevenly overestimated by the met model, the monthly NLDN based algorithm produced more LTNG NO than the hourly NLDN based algorithm during summer, while the linear regression algorithm produced least LTNG NO for summer 2011.

Focal Points for this Presentation Focus on July when the lightning events are most active. Look at the model performance across entire domain as well as regions as shown on the map. Identified two regions SE and RM: O3 mixing ratios are mostly over predicted in SE, while mostly under predicted in RM.

Hourly NLDN Strikes Layer 1 O3 (hNLDN – BASE) Animation of lightning strikes and surface O3 difference Hourly NLDN Strikes Layer 1 O3 (hNLDN – BASE)

Time series of Daily Maximum 8-hr (DM8HR) O3 Domain RM 2011

Time Series of Daily Mean NOX Domain (341) RM (68) For surface NOX mixing ratios, all the model cases are basically on top of each other; the numbers in the parentheses are the number of AQS sites

Diurnal Profiles of O3 and NOX (July, 2011) Domain SE RM O3 NOX

O3 bias difference between hNLDN and Base The bias differences show a strong regional pattern: bias increases mainly in southeast and decrease in the Rocky Mountains. Overall the MB decreases 61.2% of the AQS sites (shown in cool colors).

O3 bias difference between hNLDN and mNLDN Compare the hNLDN case with mNLDN case, the majority of the sites indicate bias decrease (79.2%).

O3 bias difference between hNLDN and pNLDN, and pNLDN and mNLDN hNLDN - pNLDN pNLDN - mNLDN

Basic Statistics for DM8HR O3 with Lightning Activities over Domain 13 12 11 10 Base hNLDN mNLDN pNLDN RMSE (ppb) 7 6 5 4 3 2 Base hNLDN mNLDN pNLDN MB (ppb) .85 .80 .75 .70 Base hNLDN mNLDN pNLDN R The stats are calculated by applying a filter which is the percentile of lightning strike distributions over the domain: 1. the percentiles (10 to 90) are calculated over the lightning strikes at all the sites within the domain for the month; 2. the stats are calculated only for those sites where the lightning strikes are equal or greater than the specific percentile: for instance, P60 is corresponding to all the sites where the lightning strikes >= 60% value of the lightning strike distribution. All the statistics indicate that the model case hNLDN performed better than all other cases.

Basic Statistics for DM8HR O3 with Lightning Activities over Southeast 13 12 11 10 9 8 7 6 5 4 3 .80 .75 .70 Base hNLDN mNLDN pNLDN MB (ppb) Base hNLDN mNLDN pNLDN R Base hNLDN mNLDN pNLDN RMSE (ppb) In the Southeast, all the model cases with lightning NO performed worse than the Base case except that hNLDN shows better correlation when applying a lower lightning strike filter, but among all the cases with lightning NO, hNLDN always performs the best.

Basic Statistics for DM8HR O3 with Lightning Activities over Rocky Mountains 10 9 8 7 -2 -3 -4 -5 -6 .75 .70 .65 .60 Base hNLDN mNLDN pNLDN MB R RMSE Base hNLDN mNLDN pNLDN In the Rocky Mountains, all the model cases with lightning NO performed significantly better than the Base case.

NADP/NTN Summer Total Wet Deposition of NO3 CMAQv5.02 For summer total wet deposition of NO3, CMAQv5.2 performs much better than CMAQv5.02 (mNLDN lightning NO; the increase of R2 from CMAQv5.0.2 to CAMAv5.2 is attributable to lightning assimilation) The hNLDN and mNLDN algorithms have comparable performance, both are slightly better than the pNLDN algorithm. All simulations with lightning NO perform better than that without BASE pNLDN mNLDN hNLDN

NADP/NTN Summer Total Wet Deposition of NO3 All the CMAQv5.0.2 simulations used the monthly-based NLDN (mNLDN). Even though the bias is similar between CMAQv5.02 and CMAQv5.2 mNLDN case, the R2 increased from 0.54 to 0.71 (previous slide). Again, all the simulations with lightning NO performed better than the Base case.

Summary The impact of lightning NO on surface O3 mixing ratios varies with region and depends on the base model configuration, but at majority of the sites, the model performed better when the hourly-NLDN based algorithm is implemented than the base model. The impact of lightning NO on surface NOX mixing ratios is minor. The hourly-NLDN based algorithm outperforms all other algorithms from all aspects for surface O3, especially in regions where the base model is relatively better configured. For deposition of NO3, all model cases with lightning NO performed significantly better than the base case, while the hourly-NLDN and monthly-NLDN based algorithms performed equally well.