Toward a Better Regional Ozone Forecast Over CONUS Using Rapid Data Assimilation of Clouds and Meteorology in WRF-Chem

Young Hee Ryu; Alma Hodzic; Gael Descombes; Ming Hu; Jérôme Barré

doi:10.1029/2019JD031232

Toward a Better Regional Ozone Forecast Over CONUS Using Rapid Data Assimilation of Clouds and Meteorology in WRF-Chem

Young Hee Ryu, Alma Hodzic, Gael Descombes, Ming Hu, Jérôme Barré

Department of Atmospheric Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

Accuracy of cloud predictions in numerical weather models can considerably impact ozone (O₃) forecast skill. This study assesses the benefits in surface O₃ predictions of using the Rapid Refresh (RAP) forecasting system that assimilates clouds as well as conventional meteorological variables at hourly time scales. We evaluate and compare the WRF-Chem simulations driven by RAP and the Global Forecast System (GFS) forecasts over the Contiguous United States (CONUS) for 2016 summer. The day 1 forecasts of surface O₃ and temperature driven by RAP are in better agreements with observations. Reductions of 5 ppb in O₃ mean bias error and 2.4 ppb in O₃ root-mean-square-error are obtained on average over CONUS with RAP compared to those with GFS. The WRF-Chem simulation driven by GFS shows a higher probability of capturing O₃ exceedances but exhibits more frequent false alarms, resulting from its tendency to overpredict O₃. The O₃ concentrations are found to respond mainly to the changes in boundary layer height that directly affects the mixing of O₃ and its precursors. The RAP data assimilation shows improvements in the cloud forecast skill during the initial forecast hours, which reduces O₃ forecast errors at the initial forecast hours especially under cloudy-sky conditions. Sensitivity simulations utilizing satellite clouds show that the WRF-Chem simulation with RAP produces too thick low-level clouds, which leads to O₃ underprediction in the boundary layer.

Original language	English
Pages (from-to)	13576-13592
Number of pages	17
Journal	Journal of Geophysical Research: Atmospheres
Volume	124
Issue number	23
DOIs	https://doi.org/10.1029/2019JD031232
Publication status	Published - 2019 Dec 16

Bibliographical note

Funding Information:
This study is supported by NASA-ROSES Grant NNX15AE38G. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank Chris Snyder, Rajesh Kumar, and three anonymous reviewers for their helpful comments and suggestions. We thank Mary Barth for a constructive discussion of model design associated with clouds. We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The surface meteorology data are available at this site (https://rda.ucar.edu/datasets/ds472.0/). The boundary layer height estimates and temperature data at ARM SGP site are available at this site (https://www.arm.gov/capabilities/observatories/sgp). The EPA surface ozone observation data are available at this site (https://aqs.epa.gov/aqsweb/airdata/download_files.html). The satellite cloud retrievals were downloaded from this site (https://search.earthdata.nasa.gov). The WRF-Chem simulation outputs are available here (10.5281/zenodo.3461300).

Funding Information:
This study is supported by NASA‐ROSES Grant NNX15AE38G. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank Chris Snyder, Rajesh Kumar, and three anonymous reviewers for their helpful comments and suggestions. We thank Mary Barth for a constructive discussion of model design associated with clouds. We would like to acknowledge high‐performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The surface meteorology data are available at this site ( https://rda.ucar.edu/datasets/ds472.0/ ). The boundary layer height estimates and temperature data at ARM SGP site are available at this site ( https://www.arm.gov/capabilities/observatories/sgp ). The EPA surface ozone observation data are available at this site ( https://aqs.epa.gov/aqsweb/airdata/download_files.html ). The satellite cloud retrievals were downloaded from this site ( https://search.earthdata.nasa.gov ). The WRF‐Chem simulation outputs are available here (10.5281/zenodo.3461300).

Publisher Copyright:
©2019. The Authors.

All Science Journal Classification (ASJC) codes

Atmospheric Science
Geophysics
Earth and Planetary Sciences (miscellaneous)
Space and Planetary Science

Access to Document

10.1029/2019JD031232

Cite this

@article{697fc4f7e2a84a0097708440da6180e4,

title = "Toward a Better Regional Ozone Forecast Over CONUS Using Rapid Data Assimilation of Clouds and Meteorology in WRF-Chem",

abstract = "Accuracy of cloud predictions in numerical weather models can considerably impact ozone (O3) forecast skill. This study assesses the benefits in surface O3 predictions of using the Rapid Refresh (RAP) forecasting system that assimilates clouds as well as conventional meteorological variables at hourly time scales. We evaluate and compare the WRF-Chem simulations driven by RAP and the Global Forecast System (GFS) forecasts over the Contiguous United States (CONUS) for 2016 summer. The day 1 forecasts of surface O3 and temperature driven by RAP are in better agreements with observations. Reductions of 5 ppb in O3 mean bias error and 2.4 ppb in O3 root-mean-square-error are obtained on average over CONUS with RAP compared to those with GFS. The WRF-Chem simulation driven by GFS shows a higher probability of capturing O3 exceedances but exhibits more frequent false alarms, resulting from its tendency to overpredict O3. The O3 concentrations are found to respond mainly to the changes in boundary layer height that directly affects the mixing of O3 and its precursors. The RAP data assimilation shows improvements in the cloud forecast skill during the initial forecast hours, which reduces O3 forecast errors at the initial forecast hours especially under cloudy-sky conditions. Sensitivity simulations utilizing satellite clouds show that the WRF-Chem simulation with RAP produces too thick low-level clouds, which leads to O3 underprediction in the boundary layer.",

author = "Ryu, {Young Hee} and Alma Hodzic and Gael Descombes and Ming Hu and J{\'e}r{\^o}me Barr{\'e}",

note = "Funding Information: This study is supported by NASA-ROSES Grant NNX15AE38G. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank Chris Snyder, Rajesh Kumar, and three anonymous reviewers for their helpful comments and suggestions. We thank Mary Barth for a constructive discussion of model design associated with clouds. We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The surface meteorology data are available at this site (https://rda.ucar.edu/datasets/ds472.0/). The boundary layer height estimates and temperature data at ARM SGP site are available at this site (https://www.arm.gov/capabilities/observatories/sgp). The EPA surface ozone observation data are available at this site (https://aqs.epa.gov/aqsweb/airdata/download_files.html). The satellite cloud retrievals were downloaded from this site (https://search.earthdata.nasa.gov). The WRF-Chem simulation outputs are available here (10.5281/zenodo.3461300). Funding Information: This study is supported by NASA‐ROSES Grant NNX15AE38G. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank Chris Snyder, Rajesh Kumar, and three anonymous reviewers for their helpful comments and suggestions. We thank Mary Barth for a constructive discussion of model design associated with clouds. We would like to acknowledge high‐performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The surface meteorology data are available at this site ( https://rda.ucar.edu/datasets/ds472.0/ ). The boundary layer height estimates and temperature data at ARM SGP site are available at this site ( https://www.arm.gov/capabilities/observatories/sgp ). The EPA surface ozone observation data are available at this site ( https://aqs.epa.gov/aqsweb/airdata/download_files.html ). The satellite cloud retrievals were downloaded from this site ( https://search.earthdata.nasa.gov ). The WRF‐Chem simulation outputs are available here (10.5281/zenodo.3461300). Publisher Copyright: {\textcopyright}2019. The Authors.",

year = "2019",

month = dec,

day = "16",

doi = "10.1029/2019JD031232",

language = "English",

volume = "124",

pages = "13576--13592",

journal = "Journal of Geophysical Research: Atmospheres",

issn = "2169-897X",

number = "23",

}

TY - JOUR

T1 - Toward a Better Regional Ozone Forecast Over CONUS Using Rapid Data Assimilation of Clouds and Meteorology in WRF-Chem

AU - Ryu, Young Hee

AU - Hodzic, Alma

AU - Descombes, Gael

AU - Hu, Ming

AU - Barré, Jérôme

N1 - Funding Information: This study is supported by NASA-ROSES Grant NNX15AE38G. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank Chris Snyder, Rajesh Kumar, and three anonymous reviewers for their helpful comments and suggestions. We thank Mary Barth for a constructive discussion of model design associated with clouds. We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The surface meteorology data are available at this site (https://rda.ucar.edu/datasets/ds472.0/). The boundary layer height estimates and temperature data at ARM SGP site are available at this site (https://www.arm.gov/capabilities/observatories/sgp). The EPA surface ozone observation data are available at this site (https://aqs.epa.gov/aqsweb/airdata/download_files.html). The satellite cloud retrievals were downloaded from this site (https://search.earthdata.nasa.gov). The WRF-Chem simulation outputs are available here (10.5281/zenodo.3461300). Funding Information: This study is supported by NASA‐ROSES Grant NNX15AE38G. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank Chris Snyder, Rajesh Kumar, and three anonymous reviewers for their helpful comments and suggestions. We thank Mary Barth for a constructive discussion of model design associated with clouds. We would like to acknowledge high‐performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The surface meteorology data are available at this site ( https://rda.ucar.edu/datasets/ds472.0/ ). The boundary layer height estimates and temperature data at ARM SGP site are available at this site ( https://www.arm.gov/capabilities/observatories/sgp ). The EPA surface ozone observation data are available at this site ( https://aqs.epa.gov/aqsweb/airdata/download_files.html ). The satellite cloud retrievals were downloaded from this site ( https://search.earthdata.nasa.gov ). The WRF‐Chem simulation outputs are available here (10.5281/zenodo.3461300). Publisher Copyright: ©2019. The Authors.

PY - 2019/12/16

Y1 - 2019/12/16

N2 - Accuracy of cloud predictions in numerical weather models can considerably impact ozone (O3) forecast skill. This study assesses the benefits in surface O3 predictions of using the Rapid Refresh (RAP) forecasting system that assimilates clouds as well as conventional meteorological variables at hourly time scales. We evaluate and compare the WRF-Chem simulations driven by RAP and the Global Forecast System (GFS) forecasts over the Contiguous United States (CONUS) for 2016 summer. The day 1 forecasts of surface O3 and temperature driven by RAP are in better agreements with observations. Reductions of 5 ppb in O3 mean bias error and 2.4 ppb in O3 root-mean-square-error are obtained on average over CONUS with RAP compared to those with GFS. The WRF-Chem simulation driven by GFS shows a higher probability of capturing O3 exceedances but exhibits more frequent false alarms, resulting from its tendency to overpredict O3. The O3 concentrations are found to respond mainly to the changes in boundary layer height that directly affects the mixing of O3 and its precursors. The RAP data assimilation shows improvements in the cloud forecast skill during the initial forecast hours, which reduces O3 forecast errors at the initial forecast hours especially under cloudy-sky conditions. Sensitivity simulations utilizing satellite clouds show that the WRF-Chem simulation with RAP produces too thick low-level clouds, which leads to O3 underprediction in the boundary layer.

AB - Accuracy of cloud predictions in numerical weather models can considerably impact ozone (O3) forecast skill. This study assesses the benefits in surface O3 predictions of using the Rapid Refresh (RAP) forecasting system that assimilates clouds as well as conventional meteorological variables at hourly time scales. We evaluate and compare the WRF-Chem simulations driven by RAP and the Global Forecast System (GFS) forecasts over the Contiguous United States (CONUS) for 2016 summer. The day 1 forecasts of surface O3 and temperature driven by RAP are in better agreements with observations. Reductions of 5 ppb in O3 mean bias error and 2.4 ppb in O3 root-mean-square-error are obtained on average over CONUS with RAP compared to those with GFS. The WRF-Chem simulation driven by GFS shows a higher probability of capturing O3 exceedances but exhibits more frequent false alarms, resulting from its tendency to overpredict O3. The O3 concentrations are found to respond mainly to the changes in boundary layer height that directly affects the mixing of O3 and its precursors. The RAP data assimilation shows improvements in the cloud forecast skill during the initial forecast hours, which reduces O3 forecast errors at the initial forecast hours especially under cloudy-sky conditions. Sensitivity simulations utilizing satellite clouds show that the WRF-Chem simulation with RAP produces too thick low-level clouds, which leads to O3 underprediction in the boundary layer.

UR - http://www.scopus.com/inward/record.url?scp=85076362677&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85076362677&partnerID=8YFLogxK

U2 - 10.1029/2019JD031232

DO - 10.1029/2019JD031232

M3 - Article

AN - SCOPUS:85076362677

SN - 2169-897X

VL - 124

SP - 13576

EP - 13592

JO - Journal of Geophysical Research: Atmospheres

JF - Journal of Geophysical Research: Atmospheres

IS - 23

ER -

Toward a Better Regional Ozone Forecast Over CONUS Using Rapid Data Assimilation of Clouds and Meteorology in WRF-Chem

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this