TimeResolved

1
Time-Resolved In-Depth Evaluation ofPM and PM
Precursors using CMAQ

• Robin L. Dennis
• Atmospheric Modeling Division
• U.S. EPA/ORDNOAA/ARL
• PM Model Performance Workshop
• U.S. EPA/OAQPS
• February 10-11, 2004
• Research Triangle Park, NC

2
Objectives of Diagnostic/In-DepthModel Evaluation

• Test the model to check
• Reliability of the Predictions (Right Reason)
• Right answer for the right reason
• Wrong answer for the right reason or
  understandable reason
• Right Response
• Reasonably accurate response (a major focus of
  the work)
• Separate sources of error
• Discern among
• Emissions input error
• Meteorological error
• Chemistry/aerosol physics and chemistry error
• Aid model developers in identifying and treating
  problem areas

3

• We need to understand what is behind the
  comparisons, to help interpret them.
• Importantly, we have to understand how the
  models state aligns with the real world state.
• The model as predictor.
• The model as imperfect.
• This talk will focus on the inorganic PM system.
• Focus on urban areas, where people live.
• The complementary probing with PM box models is
  very important, but will not be discussed in this
  talk.

4
Overview of Talk

• Issue of model structure, specifically
  meteorology and KZ
• Two issues relating to emissions inputs
• Reminder of the issue of oxidized nitrogen
  chemistry (total-nitrate)
• Assess the inorganic system state of the model
• A conclusion We lack critical, key measurements
  to evaluate the model system, leaving us
  partially blind in our examination of the model
  as predictor

5
Issue of Model Structure/Meteorology

• We see a persistent premature collapse of the
  boundary layer and a morning rise of the mixed
  layer that is too slow. Always been there.
• We see this with the conservative species. The
  premature collapse also exists in the rural areas.

6
Atlanta, August 1999 EC
There is a clear, rapid rise to overprediction in
the evening as the PBL collapses around 1700 EST.
7
The pattern of overprediction in the evening and
morning occurs day-in and day-out.
8
We see similar behavior for NOY and
CO, especially the evening over- prediction.
The obs rise more than for EC.
9
EC, NOY and CO have the same diurnal pattern
10
(No Transcript)
11
Suburban/ Rural NOY
Rural NOY
We also see the pattern of overprediction at
suburban/rural and rural sites
12
Suburban/ Rural CO
Rural CO
We also see the pattern of overprediction at
suburban/rural and rural sites
13

• There is a systematic problem with MM5 that leads
  to a premature collapse of the boundary layer.
  We need to be aware of how this affects
  comparisons.
• For the nighttime concentrations we have a
  situation of compensating errors. I do not think
  one should arbitrarily change CMAQs default KZ
  to get better performance statistics for O3
  without a thorough analysis for the period being
  simulated with regard to the conservative species
  like EC, CO and NOY.
• For CMAQ, concentrations during the daylight
  hours, when the atmosphere is well mixed, are the
  best for checking the model for issues such as
  bias.

14
Emission Input Issues

• EC
• We are not discerning the bias with 24-hr
  averages.
• NH3
• Our ignorance regarding ammonias diurnal profile
  is causing problems to model ammonia
  concentrations.

15
Atlanta EC
Daylight hour predicted EC concentrations are
low, indicating the EC emissions are biased low
16
Atlanta EC
The daylight hour EC underprediction is true for
almost every day of the month
17
Atlanta EC
While the synoptic-scale agreement is quite good,
the agreement of the 24-hr averages is for the
wrong reason. Emissions of EC are actually
baised low, something not discernable from 24-hr
averages.
18
Ammonia

• We use inverse modeling to set the overall
  monthly level of ammonia
• factor of 1.2 xs 1999 NEI annual average parsed
  into monthly 12ths for month of August
• Factor of 0.4 xs 1999 NEI annual average parsed
  into monthly 12ths for month of January
• Where we can test it against NHX ( NH3 NH4)
  it works pretty well.

19
Atlanta NHX
The CMAQ NHX predictions are tracking the
synoptic signal quite well, but they are not
tracking the measured diurnal pattern
20
Atlanta NHX
21
Atlanta NHX
While the NHX pattern is not as pronounced as the
EC pattern, it is most likely also caused by the
MM5 issue along with possible errors in the NHX
diurnal profile in SMOKE. How to separate?
22
Atlanta SO42-
While there is an issue with NHX, for sulfate the
diurnal pattern is inverted, the range of
variation is smaller, and model and measurements
are in much better agreement.
23
Atlanta NO3-
Diurnal biases in NHX show up as biases in
aerosol nitrate, especially in the early morning.
24
Pittsburgh, January 2002 NHX
The predicted NHX also has a more pronounced
diurnal swing in winter, with the evening peak
showing the largest deviation or bias.
25
gtgtReminderltltOxidized Nitrogen Chemistry
total-NitrateHeterogeneous N2O5 Reaction

• 2002 release of CMAQ
• Reaction probability ? 0.1 recommended by
  Dentener and Crutzen (JGR 1993)
• Makes a lot of HNO3 at night
• Recent studies show wide range of ? values
• Dependence on humidity, temperature, chemical
  composition - sulfate, nitrate, and organic
  content
• 2003 Release of CMAQ
• Reaction probability ? 0.002 - 0.02 depending
  on NO3/(SO4NO3) according to Riemer et al. (JGR
  2003) based on lab measurements of Mentel et al
  (PCCP 1999)

26
Atlanta HNO3 (average diurnal cycle)
Urban
Suburban

• HNO3 concentrations significantly reduced with
  updated CMAQ
• Must turn off all production from N2O5 to get
  down to observed levels of HNO3

27
Suburban Atlanta HNO3(average diurnal cycle)

• Daytime over-production of HNO3 is also an issue

28
Pittsburgh Winter
Atlanta Summer

• Same behavior of HNO3 overprediction is observed
  at Pittsburgh.
• The overprediction of HNO3 appears relatively
  smaller in summer
• (no daytime issue) than in winter. Winter may
  have bigger issues.

29
Pittsburgh total-NO3 January 02
Pittsburgh NHX January 02
At Pittsburgh the wintertime relative
overprediction of total-NO3 is larger than the
relative overprediction of NHX.
30
Pittsburgh total-NO3 January 02
Pittsburgh NHX January 02
Also seen in 24-hr data At Pittsburgh the
wintertime relative overprediction of total-NO3
is larger than the relative overprediction of
NHX.
31
Setup of the Inorganic System State of the Model

• What sort of problem do these biases appear to
  create in terms of setting the model up for
  predicting the PM response to emissions
  reductions?
• We will use the Gas Ratio suggested by Spyros
  Pandis to examine the system state.
• First, what do the time-resolved patterns look
  like relative to the average diurnal patterns. We
  will include model sensitivities to help us
  learn.

32
Sulfate tracks pretty well except for a few
excursions
33
(No Transcript)
34
Total-nitrate is overpredicted. Zeroing the
heterogeneous production brings total-nitrate
very close to the observations.
35
(No Transcript)
36
The NHX predictions track fairly well, but with
periods of overprediction. Not much difference
between model versions.
37
(No Transcript)
38
Gas Ratio(per S. Pandis)
Free Ammonia NHX - 2
SO42- GR ----------------------
------------------------------ Total
Nitrate HNO3(g) NO3-(p)
GR gt 1 gt HNO3 limiting 0 lt GR lt 1
gt NH3 limiting GR lt 0 gt NH3
severely limiting (cant form NH4NO3)
Calculated in Molar Units
39
(No Transcript)
40
(No Transcript)
41
(No Transcript)
42
(No Transcript)
43
(No Transcript)
44
The CMAQ O3 Release looks best, even though it
has clear biases
45
Observations

• Gas ratio analysis shows that the model will need
  a right combination of off-setting errors to
  come close to the control response state of the
  atmosphere. May require some bias.
• Uncertainty in the ammonia inventory is a serious
  issue. PM predictions are very sensitive to
  errors in the NHX. Get NHX. We need a top-down
  engineering exam using measurements.
• Other sources of error combine differently with
  the MM5 or meteorological error, so that the
  errors do not consistently affect the PM
  predictions across different sections of the
  diurnal cycle. Errors are not necessarily
  consistent across space (needs to be further
  tested).
• We have a dilemma. Do we want the model to look
  good use official inputs? Or Do we want the
  model to be a good predictor?

46
Observations (cont.)

• High time resolution is necessary to check for
  bias. Agreement on 24-hr averages may be for the
  wrong reason.
• Comparisons must include and involve multiple
  species, including conservative tracers.
• It is important to assess the models state
  relative to emissions changes. Currently this is
  not possible because we are missing key gas
  species and the temporal coverage is inadequate.
• Without measurements of NH3 and HNO3 to go along
  with aerosol measurements (forgetting size for
  the moment) we are walking into the SIP process
  partially blind as to the quality of the models.
• Need NH3 and HNO3 24-hr averages, minimum,
  preferably hourly.
• Need measurements every day, all seasons.