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

Abstract

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 urbanrural-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.

Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

“…Thus, WS10 in analyses is sensitive to different ABL schemes due to differences in the representation of the surface process in different ABL models. The normalized standard deviation of T2 is smaller than 1.0 ( Figure 2), which might be because the WRF model has a tendency to have a warm bias during the coolest time of night and a cool bias during the warmest time of day, which is consistent with the findings of a previous study [29]. Results are similar for 1-24-h forecasts (Figure 3).…”

“…The first cycle gets the initial condition from the large-scale model (e.g., NAM or GFS), and the following cycle gets the initial condition from the restart file from the previous cycle. The results are verified by World Meteorological Organization (WMO) and NCEP Meteorological Assimilation Data Ingest System (MADIS) station observations [29]. Locations of the 344 surface stations in Domain 3 are given in Figure 1c.…”

“…Surface variables (e.g., wind) can also be sensitive to the land surface model, land use and topography [24][25][26][27][28]. Data assimilation has an impact on atmospheric or oceanic models in general, and it also plays an important role in WRF model simulations [29]; post-processing can also improve the results [30]. However, further studies of the sensitivities of the different schemes, particularly those that are newly implemented, are needed.…”

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

“…RTFDDA is formulated to continuously assimilate diverse weather data, including the rawinsonde, metar, ship, buoy reports, wind profilers, satellite atmospheric motion vectors, commercial airline reports, and radar, into the WRF model by a nudging/Newtonian relaxation approach. During the DA periods, it calculates the differences between the observation and model states, and then nudges the model state toward the observation by adding a tendency term to the model prognostic equations with temporal and spatial weight functions (Cheng et al, ; Liu, Warner, Astling et al, ; Liu, Warner, Bowers et al, ; Pan et al, ; Sharman et al, ).…”

Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

“…Liu et al [30][31][32] applied this method to WRF and developed a real-time four-dimensional data assimilation system (RTFDDA). RTFDDA supports over 20 short-term forecast systems in the world [33][34][35][36].…”