TY - JOUR
T1 - Improving simulation of the fog life cycle with high-resolution land data assimilation
T2 - A case study from WiFEX
AU - Parde, Avinash N.
AU - Ghude, Sachin D.
AU - Sharma, Ashish
AU - Dhangar, Narendra G.
AU - Govardhan, Gaurav
AU - Wagh, Sandeep
AU - Jenamani, R. K.
AU - Pithani, Prakash
AU - Chen, Fei
AU - Rajeevan, M.
AU - Niyogi, Dev
N1 - Funding Information:
We thank the Director, IITM , Pune, India for his encouragement during this study. The authors acknowledge Goddard Earth Science Data and Information Service Centre (GES DISC) for providing GLDAS data and ISRO for making remote-sensed soil moisture data available. We thank Shri. Narendra Nigam, India Meteorological Department (IMD) , for availing visibility data. The authors are thankful to the MODIS satellite and Wyoming radiosonde data site for their public access. The authors would like to thank the technical and computer staff of Aditya High-Performance computing system, IITM, Pune, India. The authors would like to acknowledge all the members of the WiFEX for taking the extensive field observations during the 2017-18 campaign. AS acknowledges the support of the Prairie Research Institute at the University of Illinois Urbana-Champaign and NSF award 2139316. DN was supported by the John E. "Brick" Elliott Centennial Endowment at the University of Texas at Austin, and the NSF 1835739 U-Cube project, and NASA Interdisciplinary Sciences awards 80NSSC20K1262 and 80NSSC20K1268. We thank the anonymous reviewers for the critical reviews and the comments that helped us to improve the manuscript.
Funding Information:
We thank the Director, IITM, Pune, India for his encouragement during this study. The authors acknowledge Goddard Earth Science Data and Information Service Centre (GES DISC) for providing GLDAS data and ISRO for making remote-sensed soil moisture data available. We thank Shri. Narendra Nigam, India Meteorological Department (IMD), for availing visibility data. The authors are thankful to the MODIS satellite and Wyoming radiosonde data site for their public access. The authors would like to thank the technical and computer staff of Aditya High-Performance computing system, IITM, Pune, India. The authors would like to acknowledge all the members of the WiFEX for taking the extensive field observations during the 2017-18 campaign. AS acknowledges the support of the Prairie Research Institute at the University of Illinois Urbana-Champaign and NSF award 2139316. DN was supported by the John E. “Brick” Elliott Centennial Endowment at the University of Texas at Austin, and the NSF 1835739 U-Cube project, and NASA Interdisciplinary Sciences awards 80NSSC20K1262 and 80NSSC20K1268. We thank the anonymous reviewers for the critical reviews and the comments that helped us to improve the manuscript.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/11
Y1 - 2022/11
N2 - The present study highlights the role of high-resolution land data assimilation in improving the prediction of the radiation fog and near-surface meteorological variables. The performance of the Weather Research and Forecasting (WRF) model coupled with the High-Resolution Land Data Assimilation System (HRLDAS) is evaluated for a dense fog event that occurred on 24–25 January 2018 using detailed observations from the Winter Fog EXperiment (WiFEX) over the Delhi (India) region. The Noah-MP Land-Surface Model (LSM) based HRLDAS framework was executed in uncoupled mode to develop fine-grid soil states for soil moisture (SM) and soil temperature (ST) covering the Indo Gangetic Plain (IGP) region at 2 km horizontal grid resolution. The quality of soil states (SM/ST) from the HRLDAS and reanalysis (CNTL) dataset was verified with observed SM/ST at the Indira Gandhi International (IGI) airport, New Delhi, during 2017–18 winter months (December–January). It was found that the soil state in the CNTL dataset is moist, despite the actual soil condition being dryer at the observation site. HRLDAS simulated SM reasonably agrees with observations at IGI by reducing wet mean bias by about 56%. Subsequently, four sensitive experiments were carried out using Noah-MP (NM) and Pleim-Xiu (PX) land-surface parameterisations in the WRF model initialised with CNTL and HRLDAS soil states. We found that the bias in micro-meteorological variables (T2, RH2, WS10) and Turbulent Kinetic Energy (TKE) during the fog event was significantly improved with Pleim-Xiu (PX) land-surface parameterisations and HRLDAS soil states (HRLDAS_PX). Statistical performance of the micro-meteorological variables (T2, RH2 and WS10) exhibited low variance (0.13°C, 0.10% and 0.75 m s−1), high correlation (0.93, 0.92 and 0.82) and high index of agreement (0.92, 0.87 and 0.78). As a result, the error in fog onset timing was notably reduced to 02 h, and the vertical representation of fog was skillfully demonstrated in the HRLDAS_PX simulation. The sensitivity experiments revealed that there is a need to revisit soil states to improve the skill of the fog forecast.
AB - The present study highlights the role of high-resolution land data assimilation in improving the prediction of the radiation fog and near-surface meteorological variables. The performance of the Weather Research and Forecasting (WRF) model coupled with the High-Resolution Land Data Assimilation System (HRLDAS) is evaluated for a dense fog event that occurred on 24–25 January 2018 using detailed observations from the Winter Fog EXperiment (WiFEX) over the Delhi (India) region. The Noah-MP Land-Surface Model (LSM) based HRLDAS framework was executed in uncoupled mode to develop fine-grid soil states for soil moisture (SM) and soil temperature (ST) covering the Indo Gangetic Plain (IGP) region at 2 km horizontal grid resolution. The quality of soil states (SM/ST) from the HRLDAS and reanalysis (CNTL) dataset was verified with observed SM/ST at the Indira Gandhi International (IGI) airport, New Delhi, during 2017–18 winter months (December–January). It was found that the soil state in the CNTL dataset is moist, despite the actual soil condition being dryer at the observation site. HRLDAS simulated SM reasonably agrees with observations at IGI by reducing wet mean bias by about 56%. Subsequently, four sensitive experiments were carried out using Noah-MP (NM) and Pleim-Xiu (PX) land-surface parameterisations in the WRF model initialised with CNTL and HRLDAS soil states. We found that the bias in micro-meteorological variables (T2, RH2, WS10) and Turbulent Kinetic Energy (TKE) during the fog event was significantly improved with Pleim-Xiu (PX) land-surface parameterisations and HRLDAS soil states (HRLDAS_PX). Statistical performance of the micro-meteorological variables (T2, RH2 and WS10) exhibited low variance (0.13°C, 0.10% and 0.75 m s−1), high correlation (0.93, 0.92 and 0.82) and high index of agreement (0.92, 0.87 and 0.78). As a result, the error in fog onset timing was notably reduced to 02 h, and the vertical representation of fog was skillfully demonstrated in the HRLDAS_PX simulation. The sensitivity experiments revealed that there is a need to revisit soil states to improve the skill of the fog forecast.
KW - Land-surface data assimilation
KW - Radiation fog
KW - Soil moisture
KW - Soil temperature
KW - WiFEX
UR - http://www.scopus.com/inward/record.url?scp=85134432383&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134432383&partnerID=8YFLogxK
U2 - 10.1016/j.atmosres.2022.106331
DO - 10.1016/j.atmosres.2022.106331
M3 - Article
AN - SCOPUS:85134432383
SN - 0169-8095
VL - 278
JO - Atmospheric Research
JF - Atmospheric Research
M1 - 106331
ER -