10 th jcsda workshop on satellite data assimilation,  college park, md, october 10-12, 2012

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Implementation of GOES Total Column Ozone Assimilation within NAM-CMAQ to improve Operational Air Quality Forecasting capabilities R. Bradley Pierce (NOAA/NESDIS/STAR) Todd Schaack, Allen Lenzen, (UW-Madison, CIMSS) Pius Lee (NOAA/OAR/ARL) 10 th JCSDA Workshop on Satellite Data Assimilation, College Park, MD, October 10-12, 2012

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Implementation of GOES Total Column Ozone Assimilation within NAM-CMAQ to improve Operational Air Quality Forecasting capabilities R. Bradley Pierce (NOAA/NESDIS/STAR) Todd Schaack, Allen Lenzen, (UW-Madison, CIMSS) Pius Lee (NOAA/OAR/ARL). - PowerPoint PPT Presentation

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Page 1: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Implementation of GOES Total Column Ozone Assimilation within NAM-CMAQ to improve Operational Air Quality Forecasting

capabilities

R. Bradley Pierce(NOAA/NESDIS/STAR)

Todd Schaack, Allen Lenzen, (UW-Madison, CIMSS)

Pius Lee(NOAA/OAR/ARL)

10th JCSDA Workshop on Satellite Data Assimilation, College Park, MD, October 10-12, 2012

Page 2: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

OutlineImpact of improved vertical resolution in NAM-CMAQ on GOES Total Column Ozone assimilation

Improved Lateral Boundary Conditions for NAM-CMAQ AQ forecasts

Next Steps: Assimilation of hyperspectral O3 retrievals and radiances within GDAS

Summary

Directions (9th JCSDA Workshop)

Work with the NOAA Air Resources Lab (ARL) and the National Air Quality Forecasting Capability (NAQFC) team to test impact of improved vertical resolution in

NAM-CMAQ

Coarse vertical resolution within Operational NAM-CMAQ leads to increased biases in surface ozone over the inter-mountain west

Work with the NASA Air Quality Applied Science Team (AQAST) to test the use of climatological ozone P-L improve the GFS tropospheric distributions and provide time-dependent lateral boundary conditions to NAM-CMAQ

Page 3: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Focus on 56 level “tightly coupled” NAM-CMAQ/GSI GOES Total Column Ozone (TCO) experiments during July 2011*

Addresses impact of coarse vertical resolution within Operational NAM-CMAQ

Facilitates alignment with real-time parallel runs using NMMB being conducted by NAQFC

Allows use of NASA DISCOVER-AQ airborne insitu data and ozonesondes for validation

NAM-CMAQ/GSI experiments began on June 22th, 2011 and extend to July 31th, 2011 with June 22-June 30st, 2011 being treated as a spin-up period.

CMAQ version 4.6 with adjusted surface deposition and including wildfire emissions from the U.S. Forest Service’s Pacific Wildland Fire Sciences Laboratory BLUESKY framework

* Based on discussions with Ivanka Stajner (NWS/OST Program Manager for NAQFC) following the 9 th JCSDA workshop

Impact of improved vertical resolution in NAM-CMAQ

Page 4: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

July 2011 56 level NMMB-CMAQ/GSI GOES Total Column Ozone (TCO) assimilation experiments

Conducted using the following configurations:

loose and tight coupling of the CMAQ vertical coordinate with the NMMB vertical grid

with and without GFS Lateral Boundary Conditions (LBC) With and without GOES TCO assimilation with and without using GFS ozone distributions to constrain the upper

troposphere/lower stratosphere within the CMAQ model domain

These experiments were used to determine the optimal way to combine the NAM-CMAQ and GFS ozone distributions to obtain the best background for GOES TCO assimilation within GSI.

Cycling experiments used GOES East and GOES West TCO with bias correction obtained from co-located GOES/OMI TCO measurements

Cycling experiments used updated ozone background error covariances that combine NMMB-CMAQ background errors (NMC method) with default GFS

Page 5: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Baseline: Tightly coupled 56 level NMMB-CMAQ/No GOES TCO assimilation/static LBC NMMB-CMAQ significantly underestimates ozone mixing ratios and variability above 300mb and shows a relatively poor correlation (r=0.51) with the DISCOVER-AQ ozonesonde measurements.

CMAQ

Ozo

ne

Stati

c L

BC

Page 6: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

NMMB-CMAQ significantly overestimates ozone mixing ratios between 300-100mb and variability below 200mb but shows an improved correlation (r=0.75) with the DISCOVER-AQ ozonesonde measurements.

GFS LBC: Tightly coupled 56 level NMMB-CMAQ/No GOES TCO assimilation/GFS LBC

CMAQ

Ozo

ne

GFS

LBC

Page 7: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

NMMB-CMAQ still overestimates ozone mixing ratios between 300-100mb and variability below 200mb

GOES TCO: Tightly coupled 56 level NMMB-CMAQ/With GOES TCO assimilation/GFS LBC.

GFS

Ozo

neCM

AQ O

zone

GFS

LBC

GSI GOES TCO Assimilation

Page 8: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

GFS:

GFS

Ozo

ne

GFS UT/LS high biases (~30%) are significantly less then NMMB-CMAQ when GFS LBC are used. This points to an issue with NMMB-CMAQ UT/LS ozone transport even with 56 level tightly coupled framework. Possibly due to diffusive nature of vertical advection and sharp gradient in UT/LS.

Page 9: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Constraining NMMB-CMAQ with GFS UT/LS ozone mixing ratios reduces biases and variability below 200mb and significantly improves correlation (r=0.95) with the DISCOVER-AQ ozonesonde measurements.

GFS UT/LS: Tightly coupled 56 level NMMB-CMAQ/ With GOES TCO assimilation/GFS LBC/GFS UT/LS

GFS

Ozo

neCM

AQ O

zone

GFS

LBC

GFS UT/LS

GSI GOES TCO Assimilation

Page 10: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Lin Zhang (Harvard) provided GEOS-Chem model output for July 2011 and Meiyun Lin (Princeton) provided annual GFDL AM3 model output for 2010

3hr averaged 3D ozone production rate and loss frequency, surface deposition 3hr 3D cloud fraction, optical depths, temperature

Monthly mean 3D merged GFS (stratosphere) + GEOS-CHEM (troposphere) ozone PL was developed for use in GFS ozone physics module which was adapted for inclusion of 3D O3 P-L.

July 2011 GDAS cycling experiments conducted using default GFS O3 PL (Baseline) and with 3D diurnally varying merged GFS/GEOS-CHEM O3 PL

Same approach could be used for implementation of CO2, CH4, N2O, and CO prediction within GFS thereby aligning GFS with CRTM/GSI development

Improved Lateral Boundary Conditions for Operational NAM-CMAQ AQ forecasts*

*Funded through NASA Air Quality Applied Science Team (AQAST) FY12 Tiger team to support JCSDA hyperspectral (AIRS, IASI, CrIS) ozone assimilation (retrieval and radiance )

Page 11: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Default GFS July O3 PL

Tropospheric O3 Production maximum at 700mb/70N is unrealistic

Tropospheric O3 Loss uniform below 700mb with maximum at 70N is unrealistic

Page 12: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

July 2011 3D merged GFS + GEOS-CHEM O3 PL

Tropospheric O3 Production maximum at surface over NH industrial regions (30-50N) is realistic

Tropospheric O3 Loss maximum at surface over subtropical NH water vapor (and OH) maximum is realistic

Page 13: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

GDAS surface O3 July 2011

3D Diurnal O3 PL Default (Control)

Page 14: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

GDAS ControlUT/LS high biases (~30%) and tropospheric low biases compared to DISCOVER-AQ ozonesondes. Underestimate in UT/LS and PBL variability.

Def

ault

O3

PL

Page 15: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

GDAS 3D Diurnal O3 PLSlight reduction in UT/LS high biases (~20%) and significant reduction in PBL bias with improved PBL variability compared to DISCOVER-AQ ozonesondes.

Def

ault

O3

PL3D

Diu

rnal

O3

PL

Page 16: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Northern Hemisphere

Anomaly Correlation die off curve at 500 hPa 1 July 2011 – 31 July 2011

Southern Hemisphere

No statistically significant differences in NH or SH 500hPa 0-5 day Height FX at the 95% confidence level.

FIX2=3D Diurnal O3 PL

Page 17: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Global Temperature Biases1 July 2011 – 31 July 2011

Increase in cold bias at 50hPa and decrease in 10hPa warm bias in 3D diurnal O3 PL experiment (no significant changes below 200mb)

FIX2=3D Diurnal O3 PL

Page 18: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Global and Tropical 50hPa Temperature Bias Drop-off1 July 2011 – 31 July 2011

Global Tropics

Statistically significant increase in global 50hPa Temperature cold bias largely explained by increased tropical cold bias (3D diurnal O3 PL worse then Control)

FIX2=3D Diurnal O3 PL

Page 19: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Global and SH 10hPa Temperature Bias Drop-off1 July 2011 – 31 July 2011

GlobalSouthern

Hemisphere

Statistically significant decrease in global 10hPa Temperature warm bias largely explained by decreased SH warm bias (3D diurnal O3 PL better then Control)

FIX2=3D Diurnal O3 PL

Page 20: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Next Steps: Assimilation of hyperspectral O3 retrievals and radiances

within GDASAssimilation of ozone measurements will consider both operational ozone retrievals and assimilation of selected channels influenced by ozone absorption (980-1080 cm-1)

Assimilation of the ozone retrievals will include application of retrieval averaging kernels to the GFS first guess within the observation operator

GDAS 3D Diurnal O3 PL

AIRS instrument O3 sensitivity functions

Page 21: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

SummaryJuly 2011 NMMB-CMAQ/GFS GOES TCO experiments show that it is necessary to use GFS constraints in the NMMB-CMAQ UT/LS

UT/LS high biases of ~60% still remain and are 2x larger then GFS biases in this region (CMAQ V4.7.1 and beyond uses PPM vertical advection to limit numerical diffusion which may mitigate this issue)

Mid-tropospheric low biases of ~30% still remain and are comparable to GFS biases in this region (may be due to lack of convective mixing of high PBL ozone into mid-troposphere).

3D diurnally varying tropospheric O3 Production and Loss has been implemented into GFS and GDAS cycling experiments have been conducted during July 2011.

Results in slight reduction in UT/LS O3 high biases (~20%) and significant reduction in PBL bias with improved PBL variability compared to DISCOVER-AQ ozonesondes.

No statistically significant differences in NH or SH 500hPa 0-5 day Height forecast skill was found but statistically significant increases in 50hPa cold biases and reductions in 10hPa warm biases were found.

These experiments will be used as baseline for assessment of the impact of assimilation of hyperspectral IR O3 radiances and retrievals.

Page 22: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

Extra Slides

Page 23: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

FindingsGSI GOES Ozone assimilation experiments during the 2010 NOAA CalNex field experiment showed that assimilation of GOES Total Column Ozone (TCO) can lead to improved representation of upper tropospheric and lower stratospheric ozone within NAM-CMAQ

However, coarse vertical resolution within Operational NAM-CMAQ leads to overestimates in decent of high ozone into the lower troposphere and increased biases in surface ozone over the inter-mountain west

DirectionsWork with the NOAA Air Resources Lab (ARL) and the National Air Quality Forecasting Capability (NAQFC) team to test impact of improved vertical resolution in NAM-CMAQ

Work with the NASA Air Quality Applied Science Team (AQAST) to test the use of climatological ozone P-L improve the GFS tropospheric distributions and provide time-dependent lateral boundary conditions to NAM-CMAQ

Summary of Status at 9th JCSDA Workshop (May 24-25, 2011, College Park MD)

Page 24: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

The NMMB-CMAQ surface ozone forecast conducted on S4 is in very good agreement with NCO: 99.11% of the S4 48hr surface ozone prediction is within 0.001% of the NCO prediction after 8 days of forecast cycling.

NMMB-CMAQ S4 benchmarking experiments

The 22 level operational NMMB-CMAQ and associated preprocessors (BLUESKY, SMOKE, PREMAQ) from the NCEP Central Operations (NCO) was ported onto the NESDIS S4 computer at UW-Madison and benchmarked with respect to NCO parallel runs (referred to as B1A Fire1 at NCO).

Page 25: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

GDAS 3D Diurnal O3 PLSlight reduction in UT/LS high biases (~20%) and significant reduction in PBL bias with improved PBL variability compared to DISCOVER-AQ ozonesondes.

Page 26: 10 th  JCSDA Workshop on Satellite Data Assimilation,  College Park, MD, October 10-12, 2012

RAQMS explicit O3 PL: MLS stratospheric profile/OMI TCO assimilationGDAS biases with 3D monthly mean diurnal O3 P-L is comparable to RAQMS explicit O3 PL. UT/LS variability is not captured in GDAS. GDAS mid-tropospheric low bias most likely due to lack of convective mixing of high O3 out of continental PBL within GDAS