Evaluation of WRF capability to detect dry and wet periods in Spain using drought indices

Abstract

The Weather Research and Forecasting (WRF) model has been used to show the benefits provided by downscaled fields to detect and analyze wet and dry periods over a region with high precipitation variability such as Spain. We have analyzed the spatiotemporal behavior of two widely used drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), computed at 3 and 12 month time scales, which provide important information in an agricultural and water‐resource context. These two indices were computed from WRF outputs and compared with those calculated from observational (monthly precipitation and temperature databases of Spain, MOPREDAS and MOTEDAS) and from the European Centre for Medium‐Range Weather Forecasts Interim Re‐Analysis (ERA‐Interim) data sets. This evaluation was made by using a regional scale and a multistep regionalization method and by comparison of individual grid points. In general, results indicate that the drought indices obtained by using WRF outputs provide a noticeable improvement regarding those computed by using ERA‐Interim, higher at longer time scales. Although results show no significant differences between drought indices analyzed, the improvement offered by WRF is greater for SPI than for SPEI. In terms of averaged duration, magnitude, and severity of drought, the benefits provided by WRF are not so evident, presenting better agreement with the observational data at 12 month time scale, being clearer for the intensity. These findings evidence the benefit of using WRF climate fields to monitor, analyze, and detect drought events, being a valuable source of knowledge for a suitable decision making, especially for water‐resource management.

Radiance‐Based Evaluation of WRF Cloud Properties Over East Asia: Direct Comparison With FY‐2E Observations

“…This study evaluates the cloud properties given by the Advanced Research core of the WRF model (Skamarock et al, ), and its results are coupled with the CRTM to generate synthetic images of satellite‐observed BTs. The WRF, a sophisticated compressible and nonhydrostatic model, is widely applied for weather forecasting and research (García‐Valdecasas‐Ojeda et al, ; Kala et al, ), and it combines a numerical weather prediction model, atmospheric model, and data assimilation to better simulate and forecast mesoscale weather processes. Prognostic equations for various cloud microphysical characteristics and information of dry air are included.…”

The Importance of Soil‐Type Contrast in Modulating August Precipitation Distribution Near the Edwards Plateau and Balcones Escarpment in Texas

“…Successfully constraining the mesoscale simulation to follow the synoptic-scale driving fields by spectral nudging was key to reproducing the precipitation in the southern Great Plains in our case. This benefit, while maintaining the ability of the mesoscale models to develop small-scale dynamics, allowed successful applications of spectral nudging in dynamical downscaling of precipitation (García-Valdecasas Ojeda et al, 2017;Huang et al, 2016;Liu et al, 2012;Lo et al, 2008;Mabuchi et al, 2002;Miguez-Macho et al, 2004;Paul et al, 2016;Spero et al, 2014;von Storch et al, 2000). To achieve the best simulation of August precipitation climatology in this study, we used the spectral nudging configurations (including nudging strength, nudging height, and wave numbers) as suggested by Wang and Kotamarthi (2014) on the WRF downscaling simulations for the August of all 14 years.…”

Variability and Factors of Influence of Extreme Wet and Dry Events in Northern Mexico

“…Given this, in this study for the identification of extreme wet events, the three-month scale (SPEI-3) was applied as it reflects short and medium term seasonal soil moisture conditions with higher resolution [17]. Specifically for the identification of extreme dry events, the 24-month scale (SPEI-24) was applied, since the bi-annual period is essential for capturing low frequency variability, avoiding the explicit annual cycle [13,14].…”