William Y. Y. Cheng

Regional Soil Moisture Biases and Their Influence on WRF Model Temperature Forecasts over the Intermountain West

AbstractOperational Weather Research and Forecasting (WRF) Model forecasts run over Dugway Proving Ground (DPG) in northwest Utah, produced by the U.S. Army Test and Evaluation Command Four-Dimensional Weather System (4DWX), underpredict the amplitude of the diurnal temperature cycle during September and October. Mean afternoon [2000 UTC (1300 LST)] and early morning [1100 UTC (0400 LST)] 2-m temperature bias errors evaluated against 195 surface stations using 6- and 12-h forecasts are –1.37° and 1.66°C, respectively. Bias errors relative to soundings and 4DWX-DPG analyses illustrate that the afternoon cold bias extends from the surface to above the top of the planetary boundary layer, whereas the early morning warm bias develops in the lowest model levels and is confined to valleys and basins. These biases are largest during mostly clear conditions and are caused primarily by a regional overestimation of near-surface soil moisture in operational land surface analyses, which do not currently assimilate in...

Impact of four-dimensional data assimilation (FDDA) on urban climate analysis

This study investigates the impact of four-dimensional data assimilation (FDDA) on urban climate analysis, which employs the NCAR (National Center for Atmospheric Research) WRF (the weather research and forecasting model) based on climate FDDA (CFDDA) technology to develop an urban-scale microclimatology database for the Shenzhen area, a rapidly developing metropolitan located along the southern coast of China, where uniquely high-density observations, including ultrahigh-resolution surface AWS (automatic weather station) network, radio sounding, wind profilers, radiometers, and other weather observation platforms, have been installed. CFDDA is an innovative dynamical downscaling regional climate analysis system that assimilates diverse regional observations; and has been employed to produce a 5 year multiscale high-resolution microclimate analysis by assimilating high-density observations at Shenzhen area. The CFDDA system was configured with four nested-grid domains at grid sizes of 27, 9, 3, and 1 km, respectively. This research evaluates the impact of assimilating high-resolution observation data on reproducing the refining features of urban-scale circulations. Two experiments were conducted with a 5 year run using CFSR (climate forecast system reanalysis) as boundary and initial conditions: one with CFDDA and the other without. The comparisons of these two experiments with observations indicate that CFDDA greatly reduces the model analysis error and is able to realistically analyze the microscale features such as urban-rural-coastal circulation, land/sea breezes, and local-hilly terrain thermal circulations. It is demonstrated that the urbanization can produce 2.5 k differences in 2 m temperatures, delays/speeds up the land/sea breeze development, and interacts with local mountain-valley circulations.

A Method to Assess the Wind and Solar Resource and to Quantify Interannual Variability over the United States under Current and Projected Future Climate

AbstractThe National Center for Atmospheric Research and the National Renewable Energy Laboratory (NREL) collaborated to develop a method to assess the interannual variability of wind and solar power over the contiguous United States under current and projected future climate conditions, for use with NREL’s Regional Energy Deployment System (ReEDS) model. The team leveraged a reanalysis-derived database to estimate the wind and solar power resources and their interannual variability under current climate conditions (1985–2005). Then, a projected future climate database for the time range of 2040–69 was derived on the basis of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) simulations driven by free-running atmosphere–ocean general circulation models. To compare current and future climate variability, the team developed a baseline by decomposing the current climate reanalysis database into self-organizing maps (SOMs) to determine the predominant modes o...

Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF‐RTFDDA

In this study, a lightning data assimilation (LDA) scheme was developed and implemented in the NCAR (National Center for Atmospheric Research) Weather Research and Forecasting – Real-Time Four-Dimensional Data assimilation (WRF-RTFDDA) system. In this LDA method, graupel mixing ratio (qg) is retrieved from observed total lightning. To retrieve qg on model grid-boxes, column-integrated graupel mass is first calculated using an observation-based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical qg vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved qg fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short-term (0 – 6 h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short-term lightning and precipitation forecasts by improving the model simulation of the qg fields, updrafts, cold pool and front locations. The improvements were most notable in the first two hours, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0 – 2 h) applications.