scholarly journals Use of Sensor Imagery Data for Surface Boundary Conditions in Regional Climate Modeling

Sensors ◽  
2011 ◽  
Vol 11 (7) ◽  
pp. 6728-6742 ◽  
Author(s):  
Hyun Il Choi
Keyword(s):  
Boundary Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Modeling ◽  
Surface Boundary ◽  
Surface Boundary Conditions ◽  
Imagery Data

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.

scholarly journals Urban Climates and Climate Change

2020 ◽  
Vol 45 (1) ◽  
pp. 411-444 ◽  
Author(s):  
Valéry Masson ◽  
Aude Lemonsu ◽  
Julia Hidalgo ◽  
James Voogt
Keyword(s):  
Climate Change ◽  
Climate Modeling ◽  
Regional Climate ◽  
Urban Climate ◽  
Regional Climate Modeling ◽  
Climate Impact ◽  
Local Climate ◽  
Urban Climatology ◽  
Recent Developments ◽  
Global Agenda

Cities are particularly vulnerable to extreme weather episodes, which are expected to increase with climate change. Cities also influence their own local climate, for example, through the relative warming known as the urban heat island (UHI) effect. This review discusses urban climate features (even in complex terrain) and processes. We then present state-of-the-art methodologies on the generalization of a common urban neighborhood classification for UHI studies, as well as recent developments in observation systems and crowdsourcing approaches. We discuss new modeling paradigms pertinent to climate impact studies, with a focus on building energetics and urban vegetation. In combination with regional climate modeling, new methods benefit the variety of climate scenarios and models to provide pertinent information at urban scale. Finally, this article presents how recent research in urban climatology contributes to the global agenda on cities and climate change.

Modeling Active Layer Depth of Permafrost Changing Surface Boundary Conditions

2020 ◽  
Author(s):  
MODI ZHU ◽  
Jingfeng Wang ◽  
Husayn Sharif ◽  
Valeriy Ivanov ◽  
Aleksey Sheshukov
Keyword(s):  
Boundary Conditions ◽  
Active Layer ◽  
Surface Boundary ◽  
Layer Depth ◽  
Surface Boundary Conditions
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