Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
Preprints
https://doi.org/10.5194/gmd-2020-201
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-201
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model experiment description paper 01 Sep 2020

Submitted as: model experiment description paper | 01 Sep 2020

Review status
This preprint is currently under review for the journal GMD.

Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP V3.8.1 over the United Arab Emirates

Oliver Branch1, Thomas Schwitalla1, Marouane Temimi2, Ricardo Fonseca3, Narendra Nelli3, Michael Weston3, Josipa Milovac4, and Volker Wulfmeyer1 Oliver Branch et al.
  • 1Institute of Physics and Meteorology, University of Hohenheim, 70593 Stuttgart, Germany
  • 2Department of Civil, Environmental, and Ocean Engineering (CEOE), Stevens Institute of Technology, New Jersey, USA
  • 3Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
  • 4Meteorology Group. Instituto de Física de Cantabria, CSIC-University of Cantabria, Santander, Spain

Abstract. Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates (UAE) where extreme events like heat waves, flash floods and dust storms are severe. Hence, accurate forecasting of quantities like surface temperatures and humidity is very important. To date, there have been few seasonal-to-annual scale verification studies with WRF at high spatial and temporal resolution.

This study employs a convection-permitting scale (2.7 km grid scale) simulation with WRF-NOAHMP, in daily forecast mode, from January 01 to November 30 2015. WRF was verified using measurements of 2 m air temperature (T-2m), dew point (TD-2m), and 10 m windspeed (UV-10m) from 48 UAE surface stations. Analysis was made of seasonal and diurnal performance within the desert, marine and mountain regions of the UAE.

Results show that WRF represents temperature (T-2m) quite adequately during the daytime with biases ≤ +1 ˚C. There is however a nocturnal cold bias (−1 to −4 ˚C), which increases during hotter months in the desert and mountain regions. The marine region has the lowest T-2m biases (≤−0.75 ˚C). WRF performs well regarding TD-2m, with mean biases mostly ≤ 1 ˚C. TD-2m over the marine region is overestimated though (0.75–1 ˚C), and nocturnal mountain TD-2m is underestimated (~ −2 ˚C). UV-10m performance on land still needs improvement, and biases can occasionally be large (1–2 m s−1). This performance tends to worsen during the hot months, particularly inland with peak biases reaching ~ 3 m s−1. UV-10m are better simulated in the marine region (bias ≤ 1 m s−1). There is an apparent relationship between T-2m bias and UV-10m bias, which may indicate issues in simulation of the daytime sea breeze. TD-2m biases tend to be more independent.

Studies such as these are vital for accurate assessment of WRF nowcasting performance and to identify model deficiencies. By combining sensitivity tests, process and observational studies with seasonal verification, we can further improve forecasting systems for the UAE.

Oliver Branch et al.

Interactive discussion

Status: open (until 27 Oct 2020)
Status: open (until 27 Oct 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Oliver Branch et al.

Data sets

Verification datasets for publication "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates" Oliver Branch, Thomas Schwitalla, Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Josipa Milovac, and Volker Wulfmeyer https://doi.org/10.5281/zenodo.3894544

MET tools statistics dataset used for publication "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates" Oliver Branch, Thomas Schwitalla, Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Josipa Milovac, and Volker Wulfmeyer https://doi.org/10.5281/zenodo.4004195

Scripts for publication "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates" Oliver Branch, Thomas Schwitalla, Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Josipa Milovac, and Volker Wulfmeyer https://doi.org/10.5281/zenodo.3894491

Oliver Branch et al.

Viewed

Total article views: 175 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
134 39 2 175 2 3
  • HTML: 134
  • PDF: 39
  • XML: 2
  • Total: 175
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 01 Sep 2020)
Cumulative views and downloads (calculated since 01 Sep 2020)

Viewed (geographical distribution)

Total article views: 119 (including HTML, PDF, and XML) Thereof 119 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 28 Sep 2020
Publications Copernicus
Download
Short summary
Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates where extreme events like heat waves, flash floods and dust storms are becoming more severe. This study employs a high resolution simulation with the WRF-NOAHMP model, and the output is compared with seasonal observation data from 50 weather stations. This type of verification is vital to identify model deficiencies and improve forecasting systems for arid regions.
Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates...
Citation