Author
Listed:
- Adil Dilawar
(State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)
- Baozhang Chen
(State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jiangsu Center for Collaborative Innovation of Geographical Information Resources Development and Application, Nanjing 210023, China)
- Lifeng Guo
(State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)
- Shuan Liu
(School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)
- Muhammad Shafeeque
(Institute of Geography, University of Bremen, 28359 Bremen, Germany
Key Lab of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)
- Arfan Arshad
(Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74075, USA)
- Yawar Hussain
(Department of Geology, University of Liege, 4000 Liege, Belgium)
- Muhammad Ateeq Qureshi
(National Center for Remote Sensing and Geo Informatics, Institute of Space Technology, Karachi Campus, Karachi 75270, Pakistan)
- Alphonse Kayiranga
(State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)
- Fei Wang
(State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)
- Simon Measho
(State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Key Lab of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)
- Huifang Zhang
(State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)
Abstract
This study investigates the relative role of land surface schemes (LSS) in the Weather Research and Forecasting (WRF) model, Version 4, to simulate the heat wave events in Karachi, Pakistan during 16–23 May 2018. The efficiency of the WRF model was evaluated in forecasting heat wave events over Karachi using the three different LSS, namely NOAH, NOAH-MP, and RUC. In addition to this we have used the longwave (RRTM) and shortwave (Dudhia) in all schemes. Three simulating setups were designed with a combination of shortwave, longwave, and LSS: E1 (Dudhia, RRTM, and Noah), E2 (Dudhia, RRTM, and Noah-MP), and E3 (Dudhia, RRTM, and RUC). All setups were carried out with a finer resolution of 1 km × 1 km. Findings of current study depicted that E2 produces a more realistic simulation of daily maximum temperature T (max) at 2 m, sensible heat (SH), and latent heat (LH) because it has higher R 2 and lower errors (BIAS, RMSE, MAE) compared to other schemes. Consequently, Noah-MP (LSS) accurately estimates T (max) and land surface heat fluxes (SH&LH) because uses multiple physics options for land atmosphere interaction processes. According to statistical analyses, E2 setup outperforms other setups in term of T (max) and (LH&SH) forecasting with the higher Nash-Sutcliffe efficiency (NSE) agreement is 0.84 (0.89). This research emphasizes that the selection of LSS is of vital importance in the best simulation of T (max) and SH (LH) over Karachi. Further, it is resulted that the SH flux is taking a higher part to trigger the heat wave event intensity during May 2018 due to dense urban canopy and less vegetated area. El Niño-Southern Oscillation (ENSO) event played role to prolong and strengthen the heat wave period by effecting the Indian Ocean Dipole (IOD) through walker circulation extension.
Suggested Citation
Adil Dilawar & Baozhang Chen & Lifeng Guo & Shuan Liu & Muhammad Shafeeque & Arfan Arshad & Yawar Hussain & Muhammad Ateeq Qureshi & Alphonse Kayiranga & Fei Wang & Simon Measho & Huifang Zhang, 2021.
"Evaluation the WRF Model with Different Land Surface Schemes: Heat Wave Event Simulations and Its Relation to Pacific Variability over Coastal Region, Karachi, Pakistan,"
Sustainability, MDPI, vol. 13(22), pages 1-23, November.
Handle:
RePEc:gam:jsusta:v:13:y:2021:i:22:p:12608-:d:679509
Download full text from publisher
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.
- Bingxue Wu & Junfeng Miao & Wen Feng, 2022.
"Impact of Land Cover Change on Mountain Circulation over the Hainan Island, China,"
Sustainability, MDPI, vol. 14(18), pages 1-22, September.
- Igor Gómez & Sergio Molina & Juan José Galiana-Merino & María José Estrela & Vicente Caselles, 2021.
"Impact of Noah-LSM Parameterizations on WRF Mesoscale Simulations: Case Study of Prevailing Summer Atmospheric Conditions over a Typical Semi-Arid Region in Eastern Spain,"
Sustainability, MDPI, vol. 13(20), pages 1-17, October.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12608-:d:679509. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.