WRF-UACM Multilayer Urban Canopy Model Description

WRF-UACM Multilayer Urban Canopy Model Description 1 Date – 16.SEP.2025(Tue) WRF-UACM Description WRF-UACM Model Development (HKUST, Hong Kong) Presenter - Dr. Utkarsh P. Bhautmage (PhD, HKUST-UNC, Hong Kong) PDRF, Department of Geography, National University of Singapore (NUS) Email: upbha[email protected].hk 2 The WRF-UACM Model Fortran Code Files are available at: Bhautmage, U. P., Pleim, J., & Fung, J. (2025). WRF-UACM Multilayer Urban Canopy Model (Fortran Code). Zenodo. https://doi.org/10.5281/zenodo.17348788 For any queries on implementing the model, contact – Dr. Utkarsh P. Bhautmage ([email protected] OR [email protected]) Structure of the Atmosphere Stable (Nocturnal) Boundary Layer ▪The troposphere can be classified into two layers : 1] Planetary Boundary Layer (PBL) [Height: 0 – 1500 m AGL] 2] Free atmosphere [Above PBL upto Troposphere Ht.] ▪PBL is further subdivided in two zones. ▪The surface layer forms upto 10 % of the overall planetary boundary layer. ▪Strong gradients of Temperature, Moisture and Wind in the surface layer. Boundary layer is defined as that part of the troposphere which is directly influenced by the presence of the earth’s surface, and responds to surface forcings with a timescale of about an hour or less. -------- Roland B. Stull (1988) 3 100 4 Urban Boundary Layer Phenomenon ▪The surface layer forms upto 10 % of the overall planetary boundary layer (i.e. nearly 100-150 m AGL). ▪Strong gradients of Temperature, Moisture and Wind in the surface layer. Intense Shear Layer •At the top of the Urban canopy. •Mean kinetic energy is converted into turbulent kinetic energy. Wake Formations •Turbulent wakes are generated by roughness elements. •Wakes efficiently mix and diffuse momentum, heat, moisture and scalers like pollutants. •Size of eddies are related to the dimensions of roughness elements. Drag •Drag occurs due to the obstruction to airflow in presence of buildings. •It mainly includes momentum drag and thermal drag due to temperature differences. Radiations • Differential heating/cooling of sunlit/shaded surfaces. • Radiation trappings in the street canyon. • Heat storage in the buildings including anthropogenic sources. Urban Effect on the Airflow 5 The WRF Model v3.8 provides 13 Nos. of planetary boundary layer physics schemes. Most commonly used PBL schemes include: 1. Yonsei University (YSU) non-local scheme (Hong et al. 2006) 2. Asymmetric Convective Model version2 (ACM2) non-local scheme (Pleim, 2007a, 2007b) 3. Mellor-Yamada-Janjic (MYJ) local scheme (Janjic, 1990, 1996, 2002) 4. Bougeault-Lacarrere (Boulac) local scheme (Bougeault and Lacarrere, 1989)TKE closure schemes The fluxes are diffused locally Nonlocal upward flux transport from the surface AND asymmetrical layer by layer downward transport from the adjacent upper layer Local upward diffusion Local vs. Nonlocal schemes: 6 Planetary Boundary Layer (PBL) Modeling Options in WRF v3.8 Local ACM1 ACM2 𝜕𝐶𝑖 𝜕𝑡 =𝑀2𝑢𝐶1−𝑀2𝑑𝑖𝐶𝑖+𝑀2𝑑𝑖+1𝐶𝑖+1∆𝑧𝑖+1 ∆𝑧𝑖+1 ∆𝑧𝑖𝐾𝑖+ Τ 1 2 𝐶𝑖+1−𝐶𝑖 ∆𝑧𝑖+ Τ 1 2 +𝐾𝑖− Τ 1 2 𝐶𝑖−𝐶𝑖−1 ∆𝑧𝑖− Τ 1 2 Asymmetric Convective Model Version2 (ACM2) PBL Scheme Upward mixing rate 𝑀2𝑢= 𝑓𝑐𝑜𝑛𝑣𝐾ℎ𝑧1+ Τ 1 2 ∆𝑧1+ Τ 1 2 ℎ−𝑧1+ Τ 1 2 where, 𝑓𝑐𝑜𝑛𝑣 = 1+𝑘−2 3 0.1𝑎 −ℎ 𝐿−1 3−1 𝐶𝑖 is the mass mixing ratio for any scalar at layer 𝑖 𝐾ℎ is the eddy diffusivity for heat 𝑘 is the von-Karman constant 𝐿 is the Monin-Obukhov length scale, 𝐿=𝑇0𝑢∗ 2 𝑔𝑘𝜃∗ ℎ is the PBL height a is constant = 7.2 Local eddy diffusion 7 Downward mixing rate --- Pleim (2007a, 2007b) •It treats the urban geometry as a flat surface. •Parameters such as soil heat capacity, thermal conductivity, surface albedo, roughness length, moisture availability are varied at the urban surface. Slab Model •Single layer approach can calculate canyon drag coefficient, friction velocity etc. •Surface skin temp. at roof, walls, road and temp. profiles, radiations also. •Reflection of radiations from the surfaces and shadowing effects of buildings in 3D. •Sensible heat flux, momentum flux is passed to the WRF-Noah land surface model. UCM (Urban Canopy Model) •Most sophisticated Multi Layer approach. •Building effects are parameterized on the grid averaged variables. •Accounts roof, walls, road effects on momentum, TKE and potential temperature. •Shadowing, trapping and reflections of radiations are considered. •BEM accounts for the anthropogenic fluxes for e.g. air conditioning etc. BEP (Building Effect Parameterization) + BEM (Building Energy Model) Slab Model UCM BEP + BEM Urban Modeling Options Used in the WRF Model at Present 8 --- Kusaka et al. (2001) --- Martilli et al. (2002) UCM : Initialization of the detailed spatial distribution of state variables, for e.g. temp. profiles within wall, roofs and roads. UCM : Specification of vast number of parameters related to the building characteristics, thermal properties, emissivity, albedo, anthropogenic heating etc. BEP : The Noah-BEP Model is coupled only to the local MYJ and Boulac schemes, and non-local YSU scheme. So cannot be used with the other non-local schemes. BEP : For full advantage of the BEP, high vertical resolution is required. So, can require more computational time and hence problem for the real-time forecasts. BEP + BEM : In general, the BEP + BEM Model runs in parallel with the WRF Model. Hence, the simulation time is always more than the conventional WRF run. Some Challenges and Drawbacks Associated with the Use of Current Urban Modeling Options in the WRF Model 9 [III] Estimation of Urban Momentum and Thermal Fluxes ▪𝐶𝐷 is the drag coefficient ▪ഥ 𝑼𝑜𝑟𝑡 is the wind speed vector orthogonal to the street direction (canyon orientation) ▪𝑅𝑎𝐻 is the aerodynamic resistance for Heat ▪𝑓𝑚 and 𝑓ℎ are scalar functions based on the atmospheric stability (taken from Louis, 1979) ▪𝐹𝐴𝑟𝑓 is the roof area fraction 16 Momentum Flux Sensible Heat Flux 𝑀𝐹ℎ𝑜𝑟𝑧 =−𝜌𝑢∗ 2෡ 𝑼=−𝜌 𝑘2 ln 𝑧 𝑧02𝑓𝑚𝑼𝑼 𝑀𝐹𝑣𝑒𝑟𝑡 =−𝜌𝐶𝐷𝑼𝑜𝑟𝑡 𝑼𝑜𝑟𝑡 ൯ 𝑼𝑜𝑟𝑡 ≡ (𝑢𝑐𝑜𝑠2𝜶 − 𝑣𝑠𝑖𝑛𝜶 ∙ 𝑐𝑜𝑠𝜶 ,− 𝑢𝑠𝑖𝑛𝜶∙ 𝑐𝑜𝑠𝜶 + 𝑣𝑠𝑖𝑛2𝜶 𝑀𝐹𝑡𝑜𝑡𝑎𝑙 = 𝑓𝑠𝑡∙𝑀𝐹ℎ𝑜𝑟𝑧+𝑓𝑟𝑓∙𝑀𝐹ℎ𝑜𝑟𝑧+𝑓𝑤𝑙∙𝑀𝐹𝑣𝑒𝑟𝑡+(1−𝑈𝑓)∙𝑀𝐹𝑛𝑜𝑛_𝑢𝑟𝑏𝑎𝑛 𝐻𝐹𝑢𝑔 =−𝜌𝑢∗𝜃∗=−𝜌 𝐶𝑝 𝑅𝑎𝐻(𝜃1−𝜃𝑢𝑔) 𝜃𝑢𝑔 =𝑇𝑢𝑔 𝑝0 𝑝ൗ 𝑅𝐶𝑝 𝐻𝐹𝑟𝑓 =−𝜌𝑟𝑓∙𝐶𝑝_𝑟𝑓 𝑘2 ln ∆𝑧 𝑧02𝑓ℎ𝑼 𝑇𝑎𝑖𝑟_𝑟𝑓− 𝑇𝑟𝑓 ∙𝑈𝑓 (𝐹𝐴𝑟𝑓) 𝐻𝐹𝑤𝑙 =−𝜂 𝜃𝑐𝑎𝑛−𝜃𝑤𝑙 ∙𝑈𝑓Τ 2𝑯 𝑾 --- Based on Masson (2000) --- Based on Martilli et al. (2002) 17 [IV] Inclusion of Thermal & Moisture Fluxes in the ACM2 PBL Scheme Urban sublayer temperature scale: 𝑇∗𝑈𝐿 =𝑤′𝜃′ 𝑠+ 𝑤′𝜃′ 𝑤𝑙+ 𝑤′𝜃′ 𝑟𝑓 𝑢∗ Urban sublayer moisture scale: 𝑄∗𝑈𝐿 =𝑤′𝑞′𝑣 𝑠+ 𝑤′𝑞′𝑣 𝑟𝑓 𝑢∗ Urban sublayer buoyancy flux: 𝑇∗𝑣𝑈𝐿 =𝑇∗𝑈𝐿 1+0.61𝑞𝑣+0.61𝜃𝑣𝑄∗𝑈𝐿 Monin Obukhov length: 𝐿= −𝑢∗ 2𝜃𝑣 𝑘𝑔𝑇∗𝑣𝑈𝐿 ▪Monin Obukhov Similarity Theory (MOST) is modified to account the urban rooftop thermal and moisture fluxes, and walls thermal fluxes . ACM2-PBL Eqn. ▪The scales have been modified to account the urban rooftop level fluxes for the heat and moisture , and walls heat fluxes in the new UACM model. ▪The term 𝑤′𝜃′ represents the covariance of the vertical velocity 𝑤 with potential temperature 𝜃. ▪Term 𝑤′𝑞′𝑣 represents the covariance of the vertical velocity 𝑤 with the moisture near the surface. 𝜕𝐶𝑖 𝜕𝑡 =𝑀2𝑢𝐶1−𝑀2𝑑𝑖𝐶𝑖+𝑀2𝑑𝑖+1𝐶𝑖+1∆𝑧𝑖+1 ∆𝑧𝑖+1 𝑉𝑖∆𝑧𝑖𝑆𝑖+1/2𝐾𝑖+ Τ 1 2 𝐶𝑖+1−𝐶𝑖 ∆𝑧𝑖+ Τ 1 2 +𝑆𝑖−1/2𝐾𝑖− Τ 1 2 𝐶𝑖−𝐶𝑖−1 ∆𝑧𝑖− Τ 1 2 +𝐹𝑆_𝑟𝑓 ∆𝑧𝑖 𝜕𝐶𝑖 𝜕𝑡 =𝑀2𝑢𝐶1−𝑀2𝑑𝑖𝐶𝑖+𝑀2𝑑𝑖+1𝐶𝑖+1∆𝑧𝑖+1 ∆𝑧𝑖+1 𝑉𝑖∆𝑧𝑖𝑆𝑖+1/2𝐾𝑖+ Τ 1 2 𝐶𝑖+1−𝐶𝑖 ∆𝑧𝑖+ Τ 1 2 +𝑆𝑖−1/2𝐾𝑖− Τ 1 2 𝐶𝑖−𝐶𝑖−1 ∆𝑧𝑖− Τ 1 2 +𝐹𝑆_𝑤𝑙 ∆𝑧𝑖∆𝑧𝑖 𝑯 WRF-UACM SETUP 18 WRF v3.8 schemes and other options Selected configuration for running UACM model Sigma levels (full) 50 (0 m, 8 m, 17.5 m, 26.5 m, 36.5 m, 46.5 m, 57 m, ....) Model top pressure 50 hPa (about 20 km AGL) Met. data (initial condition) NCEP FNL (Final Operational Global Analysis data) with spatial resolution of 1⁰ in lat. & long. and temporal resolution of 6-hrs Nested domain resolution D 1 (27 km), D2 (9 km), D3 (3 km), D4 (1 km) Microphysics WRF Single Moment 3-class simple ice scheme (D1-D4) Cumulus physics Grell – Freitas ensemble scheme (for D1 and D2 only) LW/SW radiation Goddard scheme (D1-D4) Surface clay physics Pleim -Xiu (D1-D4) Surface physics Pleim -Xiu scheme (D1-D3), Urban-Pleim-Xiu only at (D4 ) PBL physics ACM 2 (Pleim) scheme (D1-D3), New Urban-ACM2 (UACM) scheme only at D4 No . of land categories 24 (USGS) Nesting One -way nesting Coarse domain time step 60 sec (1:3 Parent time step ratio for nested domains) Surface urban physics OFF (D1-D4) Details of Configuration Setting Used in WRF v3.8 Model 19 Nested Domains Setup 20 Updated Landuse for Urban Category in WPS v3.8 Nested Domains From WUDAPT DATASET 5249 Urban Grid Cells Hong Kong Urban Morphological Parameters Dataset for the PRD Region 21 Avg. Building Height (m) Plan Area Density Frontal Area Density ❑Urban parameters affect the wind speed magnitude, wind direction, and ambient temperatures within the urban canopy layers. 22 Obtaining Street Canyon Orientation Information for each 1km x 1km grid cell Street Distribution in Hong Kong ➢Street Canyons with longer street length are accounted mainly. 𝜶=σ𝑖=1 𝑛𝜶𝑖 𝐿𝑖 σ𝑖=1 𝑛𝐿𝑖 Street Canyon Orientation 23 WRF-UACM Model Sensitivity Results 24 Ideal Case Scenarios to Test UACM Model Sensitivity to the Urban Morphological Parameters [Winter Ideal Case] ▪Period: 25 – 31 December 2010 (00UTC) ▪Duration: One week ▪Visibility: Has clear sky condition ▪Wind Dir’n: Northern, North-Eastern [Summer Ideal Case] ▪Period: 02 – 08 July 2011 (00UTC) ▪Duration: One week ▪Visibility: Has clear sky condition ▪Wind Dir’n: Southern ➢Shift of Intertropical Convergence Zone (ITCZ) season-wise affects the wind direction over the Southern China. Winter Season Summer Season 25 ➢Sensitivity of the UACM model is checked for parameters such as Average Building Height, Plan Area Density, Frontal Area Density, and Street Orientation etc. mainly on the Temperature. ➢Model sensitivity is checked by analysing the Vertical Profiles of Potential Temperature upto 210 m height AGL averaged over a case period at PRD central urban grid. ➢Central urban grids are those which have at-least 4 urban grids or more in the sidewise direction. ➢Central urban grids (regions) are more representative of the urban effect and are ideal for hypothetical case studies. ➢All UACM model simulations are carried out with 4-days cases each, with first day used as model spin up. Central Urban Grids Landuse to Test UACM Model Sensitivity to the Urban Morphological Parameters WRF Domain-4 2359 Central Urban Grid Cells 32 WRF-UACM Model Real Case Analysis Results NOTE: ➢WRF-UACM : WRF model runs with the newly developed UACM (explicit urban canopy scheme) ➢WRF-BACM : WRF model control runs with the default (base) PX-LSM and (base) ACM2 PBL schemes (i.e., model runs with the traditional tile approach over the urban grid cells. 33 Real Case Scenario Simulations of the UACM Model over the PRD Region in Southern China [Winter Month Case_W] ▪Period: 01 – 31 January 2015 (00UTC) ▪Rainy Period: 11 – 13 January ▪Clear Sky Period: 16 – 19 January ▪Wind Dir’n: Northern, North-Eastern [Summer Month Case_S] ▪Period: 01 – 31 July 2015 (00UTC) ▪Rainy Period: 6 – 9 July, 16 – 18 July, 20 – 25 July ▪Clear Sky Period: 11 – 15 July, 26 – 30 July ▪Wind Dir’n: Southern Rainy Day Rainy Day Clear Sky Day Clear Sky Day Precipitation (mm) Precipitation (mm) 34 Chosen Meteorological Station Locations for Comparing the UACM and BACM Model Results with the Observations of 10m-Wind Speed and 2m-Temperature ▪Guangdong Meteorological Service ▪Hong Kong Observatory ▪Macau Meteorological and Geophysical Bureau 60 Urban Stations 20 Rural Stations ➢Selected urban met. stations fall in the denser urban regions. ➢Rural stations are in the remote locations relatively. Met. Data From: 35 Jan2015 Month-Averaged Spatial Contour Plots of 10m-Wind Speed ▪The BACM model has overpredicted the wind speed by 3-4 m/s in the urban regions especially during the daytime period. ▪UACM model is able to create the sufficient momentum drag with the consideration of multilayer urban morphology explicitly. 02 am 02 am 02 am 02 pm 02 pm 02 pm Day Night 36 Jul2015 Month-Averaged Spatial Contour Plots of 10m-Wind Speed ▪UACM model has reduced the wind speed by 3-4 m/s in the urban regions over the BACM model. ▪The wind speed reduction is more during the daytime for e.g. at 14:00 LT than the nighttime at 02:00 LT. ▪The UACM model wind speed magnitudes have matched well with the urban station observations. 02 am 02 am 02 am 02 pm 02 pm 02 pm Day Night 37 Month Averaged Vertical Profiles of Horizontal Wind Speed Jan2015 Jul2015 ▪The vertical profile of the horizontal wind speed is entirely logarithmic in nature in case of the BACM model. ▪However, the UACM model’s vertical profile of the wind speed is no longer logarithmic within the building layers. ▪Rooftop inflection point is observed in the vertical profiles of the UACM model. ▪Above the rooftop, the wind speed vertical profile remained logarithmic. ▪The near ground wind speed is quite low in case of the UACM model during the night time. 02 pm 02 am 02 am02 pm 38 Jan2015 Month Case Jul2015 Month Case One Month Station Averaged 10m-Wind Speed Time-Series Comparison Plots ▪The UACM model has shown good correlation with the 10m-Wind Speed observations at the PRD urban station sites. 10m-WSPD 60 Stations 12 Stations 39 Jan2015 Month-Averaged Spatial Contour Plots of 2m-Temperature ▪BACM Model has over-predicted the daytime urban temperatures and night-time its very cool. ▪UACM Model has lowered the daytime urban temperatures representing the cooling effect. ▪Night-time urban heat island (UHI) effect is clearly seen with the UACM Model. 02 am 02 am 02 am 12 pm 12 pm 12 pm Day Night 40 Jul2015 Month-Averaged Spatial Contour Plots of 2m-Temperature ▪Hong Kong, Macau, and Shenzhen meteorological stations have shown good correlation. ▪The urban morphological parameters play an important role in the modeling of the ambient temperatures in the urban environment. 02 am 02 am 02 am 12 pm 12 pm 12 pm Day Night 41 Jan2015 Month-Averaged Vertical Profiles of Potential Temperature ▪Vertical profiles are averaged over 5249 urban cells. ▪In winter, profiles shown mixed layer condition within the building layers with slight convection near ground and at rooftop level during day. ▪Early morning, evening, and nighttime profiles have shown stable nature condition with higher temperatures than BACM model. (c) (d) 08 am 10 am 02 pm 06 pm 08 pm 02 am Day Night 48 UACM Anthropogenic Model Analysis 49 Estimation of Anthropogenic Fluxes in the Urban Areas 𝐻𝐹𝑟𝑓 𝐴𝑛𝑡ℎ =𝜌𝑝𝑜𝑝(0.9)𝑎𝐹0+𝑎𝐹1 𝑇𝑎𝑖𝑟_𝑟𝑓−𝑇𝑏𝑎𝑙 𝐶𝑁+𝑎𝐹2 𝑇𝑏𝑎𝑙−𝑇𝑎𝑖𝑟_𝑟𝑓 𝐻𝑁 𝐻𝐹𝑢𝑔 𝐴𝑛𝑡ℎ =𝜌𝑝𝑜𝑝(0.1)𝑎𝐹0+𝑎𝐹1 𝑇𝑎𝑖𝑟_1−𝑇𝑏𝑎𝑙 𝐶𝑁+𝑎𝐹2 𝑇𝑏𝑎𝑙−𝑇𝑎𝑖𝑟_1 𝐻𝑁 Where, 𝐻𝑁=1.0, 𝑖𝑓൝𝑇𝑏𝑎𝑙−𝑇𝑎𝑖𝑟_1>0 ℃ 𝑇𝑏𝑎𝑙−𝑇𝑎𝑖𝑟_𝑟𝑓 >0 ℃ else, 𝐻𝑁=0.0 𝐶𝑁=1.0, 𝑖𝑓൝𝑇𝑏𝑎𝑙−𝑇𝑎𝑖𝑟_1<0 ℃ 𝑇𝑏𝑎𝑙−𝑇𝑎𝑖𝑟_𝑟𝑓 <0 ℃ else, 𝐶𝑁=0.0 ▪An attempt is made to advance the UACM model by adding the building anthropogenic flux. ▪The building anthropogenic flux is a function of population density, energy consumption, and ambient temperature. ▪The flux is added at the roof and street level mainly with higher contribution (90 %) at the rooftop. ▪The balance temperature for the human comfort is set at 15 °C in the winter and 25 °C in the summer. ▪𝑎𝐹0, 𝑎𝐹1, 𝑎𝐹2, and are based upon the electricity consumption and obtained from the study conducted at the Shanghai city by Ao et al., (2018). 50 Jan2015 Month Averaged Spatial Contour Plots of 2m-Temperature ▪Balance temperature is set to 15 °C for the winter season. ▪On an average, addition of the 10% of the building anthropogenic flux at the ground has resulted into around 1-2 °C rise in the 2m-Temperature in the winter January month mainly in the densely populated urban regions of the Guangzhou and Hong Kong. 02 am 02 am 12 pm 12 pm 02 am 12 pm Day Night 51 Jul2015 Month Averaged Spatial Contour Plots of 2m-Temperature ▪Balance temperature is set to 25 °C for the summer season. ▪On an average, addition of the 10% of the building anthropogenic flux at the ground has resulted into around 1-2 °C rise in the 2m-Temperature in the summer July month mainly in the densely populated urban regions of the Guangzhou and Hong Kong. 02 am 02 am 12 pm 12 pm 02 am 12 pm Day Night 52 UACM and BEP Urban Model Comparison Analysis 53 Month Averaged Vertical Profiles of Potential Temperature Jan2015 Jul2015 ▪The BEP model’s vertical profiles of the potential temperature are mostly convective at near the ground and within the building layers in both the winter and summer seasons. ▪The night-time BEP model profiles have also shown the stable nature condition but more warming in the winter Jan month and not enough warming in the summer July month. 02 pm 02 am 02 am 02 pm 54 One Month Station Averaged Time-Series Comparison Plots of 2m-Temperature ▪UACM has captured the peaks during the clear sky days. ▪UHI well modelled by the UACM over BEP model. ▪UACM has outperformed during the clear sky days. Jan2015 Jul2015 60 Stations Clear Sky Clear Sky 55 Month Averaged Vertical Profiles of Horizontal Wind Speed Jan2015 Jul2015 ▪The BEP model has reduced the wind speed by greater extent than the UACM model. ▪Inflection point at the rooftop level is more clearly seen in the UACM model. ▪BEP model profiles have mostly remained logarithmic in nature within the building layers. 02 pm 02 am 02 am 02 pm 56 One Month Station Averaged Time-Series Comparison Plots of 10m-Wind Speed Jan2015 Jul2015 ▪The 10m-Wind Speed modelled by both the UACM and BEP models have remained similar on some days. ▪However, more reduction by the BEP model compared to the observations in general. ▪UACM model has a slight over-prediction during the early days of the last week of the July month. 12 Stations 57 SWDDIR=RADST+ 2𝐇 𝐖∙RADWL Incident Shortwave Radiation Budget within the Street Canyon ▪Equation valid as per Masson (2000). WRF-UACM in Delhi Region 64 65 WRF Nested Domain Setup over the IGP Region and USGS-Sentinel LULC D2: 1-km resolutionD1: 5-km resolution Default USGS (Old) LULC 1992-1993 ▪The default USGS landuse is updated based upon Sentinel-1 and Sentinel-2 Satellite data (Released on 28 Oct 2022) ▪Data resolution = 10 meters ▪Global Overall Accuracy: 76.7 % Urban Dominant Category in red - - - - - Dr. Utkarsh P. Bhautmage (RA,IITM) & Dr. Michael Mau Fung Wong (PDRF, Hong Kong University of Science and Technology) 66 WRF v3.8 schemes and other options Selected configuration for running UACM model Sigma levels (full) 50 (0 m, 4.13 m, 12.83 m, 21.94 m, 31.47 m, 41.42 m, 51.79 m, 62.99 m....) Model top pressure 50 hPa (about 20 km AGL) Met. data (initial condition) NCEP FNL (Final Operational Global Analysis data) with spatial resolution of 1⁰ in lat. & long. and temporal resolution of 6-hrs Nested domain resolution D 1 (5 km), D2 (1 km) Microphysics WRF Single Moment 6-class (WSM6) simple ice scheme (D1-D2) Cumulus physics OFF (D1-D2) LW/SW radiation CAM scheme (D1-D2) Surface clay physics Pleim -Xiu (D1-D2) Surface physics Pleim -Xiu scheme (D1), Urban -Pleim-Xiu scheme only at (D2) PBL physics ACM 2 (Pleim) scheme (D1), Urban -ACM2 (UACM) scheme only at (D2) No . of land categories 24 (USGS) Nesting One -way nesting Coarse domain time step 8 sec (1:2 Parent time step ratio for nested domains) Surface urban physics OFF (D1-D2) Details of Configuration Setting used in WRF v3.8 Model over the IGP Region Nested Domains Setup 67 GLOBUS Urban Data with Building Height Information GLOBUS Urban Data: Delhi Patel Nagar-Karol Bag Region, Building Heights range 3-42 m in the data - - - - - Mr. Harsh Kamath & Prof. Dev Niyogi (University of Texas, USA), Dr. Utkarsh P. Bhautmage (RA,IITM) ▪Building heights vary in the range of 3 to 235 m in the GLOBUS data GLOBUS Urban Morphology Parameters Data in Delhi Region 1-km resolution 68 ▪Urban morphological parameters at 1-km resolution are derived from the GLOBUS data such as: 1. Average building height 2. Plan area index 3. Frontal area index 4. Dominant street orientation ▪Open street-map has been used to develop dominant street orientation data 69 10m-Wind Speed Simulation Comparison of BACM & UACM Day Night ▪Wind speed is reduced by the UACM in Delhi urban region by 2-3 m/s ▪Nighttime wind speed is lower in the UACM than BACM 03 am 02 pm 03 am02 pm 70 2m-Temperature Simulation Comparison of BACM & UACM Day Night ▪In the nighttime the UHI effect has been created by the UACM model in Delhi region ▪Daytime temperatures are lowered by the UACM in the urban region 02 am02 pm 02 am02 pm 71 Ghude et al., (2022) Fog Trend over Past 40 years at IGI Airport Site in Winter Season Data source: Indian Meteorological Department (IMD) ▪General trend of foggy days have increased over past four decades at IGIA site in New Delhi ▪Number of foggy days have decreased recently when compared over past two decades 72 Urbanization Effect on Fog Formation ▪In general, with increasing urban areas, the UHI effect increases and fog trend decreases. ▪The urban warming weakens the inversion layer, causes difficulty for fog to form. ▪The visibility increases, fog onset delays and early dissipation occurs. ▪In case of radiation fog, the water vapour decreases due to less condensation. ▪Whereas in advection fog, the saturation vapour pressure of water increases (air tends to contain more water vapour and remains unsaturated). --------- Gu et al. (2019) ▪In the late morning hours, the burn off causes urban hole formation over the city area due to UHI effect. -------- Gautam and Singh (2018) MODIS Sat. Image 73 Liquid Water Content (LWC) Difference of UACM & BACM ▪Liquid water content (LWC) has been reduced over the urban areas by the UACM due to less moisture content Dense Radiation Fog Case: 29-30 Jan 2017