EGU2020-10831
https://doi.org/10.5194/egusphere-egu2020-10831
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using 2D integrated water vapor (IWV) maps derived from GPS tropospheric path delays for augmenting Weather Research and Forecast (WRF) model predictions

Yuval Reuveni1,2,3 and Anton Leontiev4
Yuval Reuveni and Anton Leontiev
  • 1Department of Physics, Ariel University, Ariel, Israel (yuvalr@ariel.ac.il)
  • 2Eastern R&D Center, Ariel, Israel
  • 3School of Sustainability, Interdisciplinary Center (IDC) Herzliya, Herzliya, Israel
  • 4Department of Electrical Engineering, Ariel University, Ariel, Israel

Water vapor (WV) is the most variable greenhouse gas in the atmosphere, which acts as a key feature in climate change studies and plays a crucial role in global warming. Its spatiotemporal distribution is necessary for understanding the hydrological cycle, and consequently can be used as an input factor in climatological studies at global, regional, and local scales. Integrated water vapor (IWV), which is defined as the amount of vertically integrated water vapor, can also augment atmospheric modeling at local and regional scales because it is frequently used in energy budget and evapotranspiration assessments. Currently, there are numerous existing atmospheric models which are able to estimate IWV amount, nevertheless, they fail to obtain extremely accurate results compared with in-situ measurements such as radiosondes. Here, we present a new methodology for improving Weather Research and Forecast (WRF) model predictions accuracy, by using data assimilation technique, which combines estimated 2D IWV regional maps, derived from GPS tropospheric path delays, along with the WRF numerical model output to generate an optimal approximation of the evolving sate of the system. This is done as opposed to pervious works, which assimilated single point measurements, either from radiosondes or GPS zenith wet delay (ZTD) estimation, demonstrating some extant of improvement in the WRF prediction accuracy compare to the standalone WRF numerical runs. Using the suggested technique, our results shows a decrease of up to 30% in the root mean square difference relative to the radiosonde data for WRF predictions assimilated with 2D GPS-IWV regional maps compare to the standalone WRF numerical runs.

How to cite: Reuveni, Y. and Leontiev, A.: Using 2D integrated water vapor (IWV) maps derived from GPS tropospheric path delays for augmenting Weather Research and Forecast (WRF) model predictions , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10831, https://doi.org/10.5194/egusphere-egu2020-10831, 2020