scholarly journals Investigation of the impact of anthropogenic heat flux within an urban land surface model and PILPS-urban

2015 ◽  
Vol 126 (1-2) ◽  
pp. 51-60 ◽  
Author(s):  
M. J. Best ◽  
C. S. B. Grimmond
Keyword(s):  
Heat Flux ◽  
Land Surface ◽  
Land Surface Model ◽  
Urban Land ◽  
Anthropogenic Heat ◽  
Surface Model ◽  
Anthropogenic Heat Flux ◽  
The Impact

scholarly journals Intercomparison between the Integrated Urban land Model and the Noah Urban Canopy Model

2020 ◽  
Author(s):  
Chunlei Meng ◽  
Junxia Dou
Keyword(s):  
Heat Flux ◽  
Solar Radiation ◽  
Human Activity ◽  
Land Surface ◽  
Land Surface Model ◽  
Urban Canopy ◽  
Urban Land ◽  
Surface Model ◽  
Urban Canopy Model ◽  
Canopy Model

Abstract. Urban land surface model (ULSM) is an important tool to study the climatic effect of human activity. Now there are two main methods to parameterize the effects of human activity, the coupling method and the integrating method. For the coupled method, the urban canopy model (UCM) was developed and coupled with the land surface model for the natural land surfaces. For the integrated method, the urban land surface model was built directly based on the traditional land surface model. In this paper, the Noah Single Layer Urban Canopy Model (Noah/SLUCM) and the Integrated Urban land Model (IUM) were compared using the observed fluxes data at the 325-meter meteorology tower in Beijing. Through the comparison, the key factors and physical processes of the urban land surface model which have significant impact on the performance of ULSM were found out. The results indicate that the absorbed solar radiation of urban surface was reduced by the solar radiation scattering, the absorption of building roof and wall, and the shading effect of urban canopy and tall buildings. Urban surface roughness length and friction velocity are important in urban sensible heat flux simulation. Urban water balance and impervious surface evaporation (ISE) are important in urban latent heat flux simulation.

Urbanisation of weather data lead for more sustainable building design: urban land surface model used to generate Typical Meteorological Year (TMY) datasets

2020 ◽  
Author(s):  
Ting Sun ◽  
Yihao Tang ◽  
Jie Xiong ◽  
Hamidreza Omidvar ◽  
Sue Grimmond
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Building Design ◽  
Building Energy ◽  
Urban Land ◽  
Reanalysis Data ◽  
Weather Data ◽  
Surface Model ◽  
The Impact

<p>Typical Meteorological Year (TMY) datasets are widely used in building energy design simulations to assess needs (cooling/heat). Currently, TMY data used are representative of the past climate (from observations) of the region and generally do not account for urban climate or building-city interactions. Here we use an urban land surface model, SUEWS (Surface Urban Energy and Water Balance Scheme) driven by ERA5 reanalysis data to bridge this gap.</p><p>Using 0.25 ° large-scale ERA5 reanalysis data (1979–2018) with SUEWS we generate an urbanised TMY (uTMY) dataset for Changsha, a city with more than 4.4 million residents in the hot-summer-cold-winter region of China, to demonstrate the proposed workflow. The SUEWS simulation are evaluated at the Leifeng site (WMO code 57687) for 2016.</p><p>Through comparison of DOE EnergyPlus simulations, we also assess the impact on design building energy consumption using uTMY and cTMY (conventional TMY) data. The building design energy needs evaluation is for a common Chinese apartment building. This should allow for more spatially explicit building design, and hence more sustainable.</p>

scholarly journals Analysis of feedback effects and atmosphere responses when 2-way coupling a hydrological land surface model with a regional climate model – a case study for the Upper-Danube catchment

2012 ◽  
Vol 9 (6) ◽  
pp. 7543-7570
Author(s):  
F. Zabel ◽  
W. Mauser
Keyword(s):  
Heat Flux ◽  
Land Surface ◽  
Regional Climate ◽  
Land Surface Model ◽  
The Other ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Danube Catchment ◽  
The Impact

Abstract. Most land surface hydrological models (LSHMs) take land surface processes (e.g. soil-plant-atmosphere interactions, lateral water flows, snow and ice) into detailed spatial account. On the other hand, they usually consider the atmosphere as exogenous driver only, thereby neglecting feedbacks between the land surface and the atmosphere. Regional climate models (RCMs), on the other hand, generally describe land surface processes much coarser but naturally include land-atmosphere interactions. What is the impact on RCMs performance of the differently applied model physics and spatial resolution of LSHMs? In order to investigate this question, this study analyses the impact of replacing the land surface model (LSM) within a RCM by a LSHM. Therefore, a 2-way coupling approach was applied for a full integration of the LSHM PROMET (1×1 km2) and the atmospheric part of the RCM MM5 (45×45 km2). The scaling interface SCALMET is used for down- and upscaling the linear and non-linear fluxes between the model scales. The response of the MM5 atmosphere to the replacement is investigated and validated for temperature and precipitation for a 4 yr period from 1996 to 1999 for the Upper-Danube catchment. By substituting the NOAH-LSM with PROMET, simulated non-bias-corrected near surface air temperature significantly improves for annual, monthly and daily courses, when compared to measurements from 277 meteorological weather stations within the Upper-Danube catchment. The mean annual bias was improved from −0.85 K to −0.13 K. In particular, the improved afternoon heating from May to September is caused by increased sensible heat flux and decreased latent heat flux as well as more incoming solar radiation in the fully coupled PROMET/MM5 in comparison to the NOAH/MM5 simulation. Triggered by the LSM replacement, precipitation overall is reduced, however simulated precipitation amounts are still of high uncertainty, both spatially and temporally. The distribution of precipitation follows the coarse topography representation in MM5, resulting in a spatial shift of maximum precipitation northwards the Alps. Consequently, simulation of river runoff inherits precipitation biases from MM5. However, by comparing the water balance, the bias of annual average runoff was improved from 21.2% (NOAH/MM5) to 4.4% (PROMET/MM5) when compared to measurements at the outlet gauge of the Upper-Danube watershed in Achleiten.

scholarly journals Evaluation of the Surface Urban Energy and Water Balance Scheme (SUEWS) at a Dense Urban Site in Shanghai: Sensitivity to Anthropogenic Heat and Irrigation

2018 ◽  
Vol 19 (12) ◽  
pp. 1983-2005 ◽  
Author(s):  
Xiangyu Ao ◽  
C. S. B. Grimmond ◽  
H. C. Ward ◽  
A. M. Gabey ◽  
Jianguo Tan ◽  
...  
Keyword(s):  
Heat Flux ◽  
Water Balance ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Anthropogenic Heat ◽  
Urban Site ◽  
Continuous Irrigation ◽  
Anthropogenic Heat Flux ◽  
Urban Energy ◽  
The Impact

Abstract The Surface Urban Energy and Water Balance Scheme (SUEWS) is used to investigate the impact of anthropogenic heat flux QF and irrigation on surface energy balance partitioning in a central business district of Shanghai. Diurnal profiles of QF are carefully derived based on city-specific hourly electricity consumption data, hourly traffic data, and dynamic population density. The QF is estimated to be largest in summer (mean daily peak 236 W m−2). When QF is omitted, the SUEWS sensible heat flux QH reproduces the observed diurnal pattern generally well, but the magnitude is underestimated compared to observations for all seasons. When QF is included, the QH estimates are improved in spring, summer, and autumn but are poorer in winter, indicating winter QF is overestimated. Inclusion of QF has little influence on the simulated latent heat flux QE but improves the storage heat flux estimates except in winter. Irrigation, both amount and frequency, has a large impact on QE. When irrigation is not considered, the simulated QE is underestimated for all seasons. The mean summer daytime QE is largely overestimated compared to observations under continuous irrigation conditions. Model results are improved when irrigation occurs with a 3-day frequency, especially in summer. Results are consistent with observed monthly outdoor water use. This study highlights the importance of appropriately including QF and irrigation in urban land surface models—terms not generally considered in many previous studies.

scholarly journals Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system

2015 ◽  
Vol 8 (6) ◽  
pp. 1857-1876 ◽  
Author(s):  
J. J. Guerrette ◽  
D. K. Henze
Keyword(s):  
Land Surface ◽  
Vertical Mixing ◽  
Model Performance ◽  
Land Surface Model ◽  
Sensitivity Study ◽  
Climate Impacts ◽  
Surface Model ◽  
Time Period ◽  
Develop Land ◽  
The Impact

Abstract. Here we present the online meteorology and chemistry adjoint and tangent linear model, WRFPLUS-Chem (Weather Research and Forecasting plus chemistry), which incorporates modules to treat boundary layer mixing, emission, aging, dry deposition, and advection of black carbon aerosol. We also develop land surface and surface layer adjoints to account for coupling between radiation and vertical mixing. Model performance is verified against finite difference derivative approximations. A second-order checkpointing scheme is created to reduce computational costs and enable simulations longer than 6 h. The adjoint is coupled to WRFDA-Chem, in order to conduct a sensitivity study of anthropogenic and biomass burning sources throughout California during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. A cost-function weighting scheme was devised to reduce the impact of statistically insignificant residual errors in future inverse modeling studies. Results of the sensitivity study show that, for this domain and time period, anthropogenic emissions are overpredicted, while wildfire emission error signs vary spatially. We consider the diurnal variation in emission sensitivities to determine at what time sources should be scaled up or down. Also, adjoint sensitivities for two choices of land surface model (LSM) indicate that emission inversion results would be sensitive to forward model configuration. The tools described here are the first step in conducting four-dimensional variational data assimilation in a coupled meteorology–chemistry model, which will potentially provide new constraints on aerosol precursor emissions and their distributions. Such analyses will be invaluable to assessments of particulate matter health and climate impacts.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

scholarly journals Assessment of Roughness Length Schemes Implemented within the Noah Land Surface Model for High-Altitude Regions

2014 ◽  
Vol 15 (3) ◽  
pp. 921-937 ◽  
Author(s):  
Donghai Zheng ◽  
Rogier van der Velde ◽  
Zhongbo Su ◽  
Martijn J. Booij ◽  
Arjen Y. Hoekstra ◽  
...  
Keyword(s):  
Heat Flux ◽  
High Altitude ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Heat ◽  
Heat Fluxes ◽  
Surface Model ◽  
Surface Heat Fluxes ◽  
Noah Land Surface Model ◽  
Better Than

ABSTRACT Current land surface models still have difficulties with producing reliable surface heat fluxes and skin temperature (Tsfc) estimates for high-altitude regions, which may be addressed via adequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer. In this study, the performance of various z0h and z0m schemes developed for the Noah land surface model is assessed for a high-altitude site (3430 m) on the northeastern part of the Tibetan Plateau. Based on the in situ surface heat fluxes and profile measurements of wind and temperature, monthly variations of z0m and diurnal variations of z0h are derived through application of the Monin–Obukhov similarity theory. These derived values together with the measured heat fluxes are utilized to assess the performance of those z0m and z0h schemes for different seasons. The analyses show that the z0m dynamics are related to vegetation dynamics and soil water freeze–thaw state, which are reproduced satisfactorily with current z0m schemes. Further, it is demonstrated that the heat flux simulations are very sensitive to the diurnal variations of z0h. The newly developed z0h schemes all capture, at least over the sparse vegetated surfaces during the winter season, the observed diurnal variability much better than the original one. It should, however, be noted that for the dense vegetated surfaces during the spring and monsoon seasons, not all newly developed schemes perform consistently better than the original one. With the most promising schemes, the Noah simulated sensible heat flux, latent heat flux, Tsfc, and soil temperature improved for the monsoon season by about 29%, 79%, 75%, and 81%, respectively. In addition, the impact of Tsfc calculation and energy balance closure associated with measurement uncertainties on the above findings are discussed, and the selection of the appropriate z0h scheme for applications is addressed.

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