Search Results
You are looking at 1 - 2 of 2 items for :
- Author or Editor: Tanya L. Spero x
- Journal of Applied Meteorology and Climatology x
- Refine by Access: Content accessible to me x
Abstract
Land-use (LU) representation plays a critical role in simulating air–surface interactions that affect meteorological conditions and regional climate. In the Noah LSM within the WRF Model, LU categories are used to set the radiative properties of the surface and to influence exchanges of heat, moisture, and momentum between the air and land surface. Previous literature examined the sensitivity of WRF simulations to LU using short-term meteorological modeling approaches. Here, the sensitivity to LU representation is studied using continental-scale dynamical downscaling, which typically uses longer temporal and larger spatial scales. Two LU datasets, the U.S. Geological Survey (USGS) dataset and the 2006 National Land Cover Dataset (NLCD), are utilized in 3-yr dynamically downscaled WRF simulations over a historical period. Precipitation and 2-m air temperature are evaluated against observation-based datasets for simulations covering the contiguous United States. The WRF-NLCD simulation tends to produce lower precipitation than the WRF-USGS run, with slightly warmer mean monthly temperatures. However, WRF-NLCD results in more notable increases in the frequency of hot days [i.e., days with temperature >90°F (32.2°C)]. These changes are attributable to reductions in forest and agricultural area in the NLCD relative to USGS. There is also subtle but important sensitivity to the method of interpolating LU data to the WRF grid in the model preprocessing. In all cases, the sensitivity resulting from changes in the LU is smaller than model error. Although this sensitivity is small, it persists across spatial and temporal scales.
Abstract
Land-use (LU) representation plays a critical role in simulating air–surface interactions that affect meteorological conditions and regional climate. In the Noah LSM within the WRF Model, LU categories are used to set the radiative properties of the surface and to influence exchanges of heat, moisture, and momentum between the air and land surface. Previous literature examined the sensitivity of WRF simulations to LU using short-term meteorological modeling approaches. Here, the sensitivity to LU representation is studied using continental-scale dynamical downscaling, which typically uses longer temporal and larger spatial scales. Two LU datasets, the U.S. Geological Survey (USGS) dataset and the 2006 National Land Cover Dataset (NLCD), are utilized in 3-yr dynamically downscaled WRF simulations over a historical period. Precipitation and 2-m air temperature are evaluated against observation-based datasets for simulations covering the contiguous United States. The WRF-NLCD simulation tends to produce lower precipitation than the WRF-USGS run, with slightly warmer mean monthly temperatures. However, WRF-NLCD results in more notable increases in the frequency of hot days [i.e., days with temperature >90°F (32.2°C)]. These changes are attributable to reductions in forest and agricultural area in the NLCD relative to USGS. There is also subtle but important sensitivity to the method of interpolating LU data to the WRF grid in the model preprocessing. In all cases, the sensitivity resulting from changes in the LU is smaller than model error. Although this sensitivity is small, it persists across spatial and temporal scales.
Abstract
The use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.
Abstract
The use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.
