Will Perturbing Soil Moisture Improve Warm-Season Ensemble Forecasts? A Proof of Concept

Abstract

Current generation short-range ensemble forecast members tend to be unduly similar to each other, especially for components such as surface temperature and precipitation. One possible cause of this is a lack of perturbations to the land surface state. In this experiment, a two-member ensemble of the Advanced Research Weather Research and Forecasting (WRF) model (ARW) was run from two different soil moisture analyses. One-day forecasts were conducted for six warm-season cases over the central United States with moderate soil moistures, both with explicit convection at 5-km grid spacing and with parameterized convection at 20-km grid spacing. Since changing the convective parameterization has previously been demonstrated to cause significant differences between ensemble forecast members, 20-km simulations were also conducted that were initialized with the same soil moisture but that used two different convective parameterizations as a reference. At 5 km, the forecast differences due to changing the soil moisture were comparable to the differences in 20-km simulations with the same soil moisture but with a different convective parameterization. The differences of 20-km simulations from different soil moistures were occasionally large but typically smaller than the differences from changing the convective parameterization. Thus, perturbing the state of the land surface for this version of WRF/ARW was judged to be likely to increase the spread of warm-season operational short-range ensemble forecasts of precipitation and surface temperature when soil moistures are moderate in value, especially if the ensemble is comprised of high-resolution members with explicit convection.

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

“…Forecasts of surface-and boundary-layer variables rely heavily on surface conditions including soil temperature and moisture (Chen et al 2001;Holt et al 2006;Sutton et al 2006), properties of land surface (land use, land cover, vegetation; Barlage et al 2010), and the coupling between such parameters within the land-surface model (LSM) and PBL parameterizations (Liu et al 2006). The model microphysics and radiation may represent additional sources of biases in temperature and precipitation.…”

Performance Characteristics of a Pseudo-Operational Ensemble Kalman Filter

“…As a consequence, flow-dependent error statistics may not produce better near-surface analyses unless the variance of these fields is larger. Sutton et al (2006) attempted to overcome the lack of variance in surface variables in a convective situation by adding uncertainty to soil moisture fields. Their results indicate that adding variance to the soil moisture showed limited improvement over using the same soil moisture fields for all ensemble members.…”

Regional Modeling of Aerosol Chemical Composition at the Puy de Dôme (France)

“…Simulated near-surface variables depend on surface conditions, including soil moisture and temperature (Sutton et al 2006), land surface characteristics (land use, land cover, vegetation), and the coupling between these surface parameters within the land-surface model (LSM) and boundary layer parameterizations (Liu et al 2006;Trier et al, 2008;Misenis et al, 2010;Noilhan et al, 2011). The parameterization of cloud microphysics and radiation may represent additional sources of biases for temperature.…”