Construction of surface boundary conditions for regional climate modeling in China by using the remote sensing data

2005 ◽  
Author(s):  
Wei Gao ◽  
Zhiqiang Gao ◽  
Hyun I. Choi ◽  
Min Xu ◽  
James Slusser
Keyword(s):  
Remote Sensing ◽  
Boundary Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Remote Sensing Data ◽  
Regional Climate Modeling ◽  
Surface Boundary ◽  
Sensing Data ◽  
Surface Boundary Conditions

scholarly journals Large-Eddy Simulations of Real-World Episodes in Complex Terrain Based on ERA-Reanalysis and Validated by Ground-Based Remote Sensing Data

2019 ◽  
Vol 147 (12) ◽  
pp. 4325-4343 ◽  
Author(s):  
Cornelius Hald ◽  
Matthias Zeeman ◽  
Patrick Laux ◽  
Matthias Mauder ◽  
Harald Kunstmann
Keyword(s):  
Remote Sensing ◽  
Boundary Conditions ◽  
Complex Terrain ◽  
Computing Time ◽  
Remote Sensing Data ◽  
Large Eddy Simulations ◽  
Small Scale ◽  
Sensing Data ◽  
Large Eddy ◽  
Local Weather

Abstract A computationally efficient and inexpensive approach for using the capabilities of large-eddy simulations (LES) to model small-scale local weather phenomena is presented. The setup uses the LES capabilities of the Weather Research and Forecasting Model (WRF-LES) on a single domain that is directly driven by reanalysis data as boundary conditions. The simulated area is an example for complex terrain, and the employed parameterizations are chosen in a way to represent realistic conditions during two 48-h periods while still keeping the required computing time around 105 CPU hours. We show by evaluating turbulence characteristics that the model results conform to results from typical LES. A comparison with ground-based remote sensing data from a triple Doppler-lidar setup, employed during the “ScaleX” campaigns, shows the grade of adherence of the results to the measured local weather conditions. The representation of mesoscale phenomena, including nocturnal low-level jets, strongly depends on the temporal and spatial resolution of the meteorological boundary conditions used to drive the model. Small-scale meteorological features that are induced by the terrain, such as katabatic flows, are present in the simulated output as well as in the measured data. This result shows that the four-dimensional output of WRF-LES simulations for a real area and real episode can be technically realized, allowing a more comprehensive and detailed view of the micrometeorological conditions than can be achieved with measurements alone.

scholarly journals Estimating the Uncertainty in a Regional Climate Model Related to Initial and Lateral Boundary Conditions

2005 ◽  
Vol 18 (7) ◽  
pp. 917-933 ◽  
Author(s):  
Wanli Wu ◽  
Amanda H. Lynch ◽  
Aaron Rivers
Keyword(s):  
Boundary Conditions ◽  
Climate Model ◽  
Initial Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Modeling ◽  
Regional Model ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
The Impact

Abstract There is a growing demand for regional-scale climate predictions and assessments. Quantifying the impacts of uncertainty in initial conditions and lateral boundary forcing data on regional model simulations can potentially add value to the usefulness of regional climate modeling. Results from a regional model depend on the realism of the driving data from either global model outputs or global analyses; therefore, any biases in the driving data will be carried through to the regional model. This study used four popular global analyses and achieved 16 driving datasets by using different interpolation procedures. The spread of the 16 datasets represents a possible range of driving data based on analyses to the regional model. This spread is smaller than typically associated with global climate model realizations of the Arctic climate. Three groups of 16 realizations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in an Arctic domain, varying both initial and lateral boundary conditions, varying lateral boundary forcing only, and varying initial conditions only. The response of monthly mean atmospheric states to the variations in initial and lateral driving data was investigated. Uncertainty in the regional model is induced by the interaction between biases from different sources. Because of the nonlinearity of the problem, contributions from initial and lateral boundary conditions are not additive. For monthly mean atmospheric states, biases in lateral boundary conditions generally contribute more to the overall uncertainty than biases in the initial conditions. The impact of initial condition variations decreases with the simulation length while the impact of variations in lateral boundary forcing shows no clear trend. This suggests that the representativeness of the lateral boundary forcing plays a critical role in long-term regional climate modeling. The extent of impact of the driving data uncertainties on regional climate modeling is variable dependent. For some sensitive variables (e.g., precipitation, boundary layer height), even the interior of the model may be significantly affected.

Surface Boundary Conditions for Mesoscale Regional Climate Models

2005 ◽  
Vol 9 (18) ◽  
pp. 1-28 ◽  
Author(s):  
Xin-Zhong Liang ◽  
Hyun I. Choi ◽  
Kenneth E. Kunkel ◽  
Yongjiu Dai ◽  
Everette Joseph ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Clay Fraction ◽  
Value Added ◽  
Quality Data ◽  
Surface Boundary ◽  
Surface Boundary Conditions ◽  
Vegetation Characteristic

Abstract This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land-cover category, and static fractional vegetation cover and varying leaf-plus-stem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe.

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