Surface Modeling Scheme Research Articles

Impact of land surface model schemes in snow-dominated arid and semiarid watersheds using the WRF-hydro modeling systems

<abstract> <p>In the past century, water demand increased extensively due to the rapid growth of the human population. Ground observations can reveal hydrological dynamics but are expensive in the long term. Alternatively, hydrological models could be utilized for assessing streamflow with historical observations as the control point. Despite the advancements in hydrological modeling systems, watershed modeling over mountainous regions with complex terrain remains challenging. This study utilized the multi-physical Weather Research and Forecasting Hydrological enhancement model (WRF-Hydro), fully distributed over the Amu River Basin (ARB) in Afghanistan. The calibration process focused on land surface model (LSM) physics options and hydrological parameters within the model. The findings emphasize the importance of LSM for accurate simulation of snowmelt–runoff processes over mountainous regions. Correlation coefficient (R), coefficient of determination (R<sup>2</sup>), Nash-Sutcliff efficiency (NSE), and Kling-Gupta efficiency (KGE) were adopted for accuracy assessment over five discharge observation stations at a daily time scale; overall performance results were as follows: R was 0.85–0.42, R<sup>2</sup> was 0.73–0.17, NSE was 0.52 to −8.64, and KGE was 0.74 to −0.56. The findings of the current study can support snowmelt process simulation within the WRF-Hydro model.</p> </abstract>

Convection-Permitting Simulations of Precipitation over the Peruvian Central Andes: Strong Sensitivity to Planetary Boundary Layer Parameterization

Abstract Regional climate dynamical downscaling at convection-permitting resolutions is now practical and has the potential to significantly improve over coarser-resolution simulations, but the former is not necessarily free of systematic biases. The evaluation and optimization of model configurations are therefore important. Twelve simulations at a grid spacing of 3 km using the WRF Model with different microphysics, planetary boundary layer (PBL), and land surface model (LSM) schemes are performed over the Peruvian central Andes during the austral summer, a region with particularly complex terrain. The simulated precipitation is evaluated using rain gauge data and three gridded precipitation datasets. All simulations correctly capture four precipitation hotspots associated with prevailing winds and terrain features along the east slope of the Andes, though they generally overestimate the precipitation intensity. The simulation using Thompson microphysics, Asymmetric Convection Model version 2 (ACM2) PBL, and Noah LSM schemes has the smallest bias. The simulated precipitation is most sensitive to PBL, followed by microphysics, and least sensitive to LSM schemes. The simulated precipitation is generally stronger in simulations using the YSU rather than the MYNN and ACM2 schemes. All simulations successfully capture the diurnal precipitation peak time mainly in the afternoon over the Peruvian central Andes and in the early morning along the east slope. However, there are significant differences over the western Amazon basin, where the precipitation peak occurs primarily in the late afternoon. Simulations using YSU exhibit a 4–8-h delay in the precipitation peak over the western Amazon basin, consistent with their stronger and more persistent low-level jets. These results provide guidance on the optimal configuration of a dynamical downscaling of global climate projections for the Peruvian central Andes.

Response surface modeling and optimization scheme of an internally cooled liquid desiccant air conditioning system

Applying an internally cooled liquid desiccant air conditioning system (LDAC) is a promising energy-saving and emission-reduction scheme for hot and humid areas. This research investigates the application of an internally cooled LDAC for performance enhancement in hot and humid areas like Hong Kong. The combined system integrates liquid desiccant dehumidification (LDD) and regenerative indirect evaporative cooling (RIEC) without a power-intensive compressor. The internally cooled LDD removes latent heat from the hot and humid air before the RIEC cools it. To ensure efficient energy utilization, the LDD captures the exhaust air to assist in the dehumidification and initial cooling of the fresh air, which alleviates the efficiency deterioration of the desiccant. An all-fresh air system is used for better indoor air quality. To optimize the performance of a system with many influencing parameters, the response surface method (RSM) and multi-objective optimization are used to optimize and assess the potential and performance of the system. The system cooling capacity (C), latent heat removal rate (Qd), and dehumidification efficiency (ηd) are used as the optimization objectives. The response surface model and desirability function approach optimize six critical parameters, achieving a 7.6% improvement in dehumidification performance with low airspeed (1.5 m/s) and high desiccant concentration (40%) during high-humidity months. Increasing the extraction ratio of the RIEC by 20% in warmer months enhances the peak cooling capacity by 23.6%. This research contributes to implementing internally cooled LDAC systems and provides insights into optimizing monthly operation patterns in hot and humid regions.

Performance of Seven Land Surface Schemes in the WRFv4.3 Model for Simulating Precipitation in the Record-Breaking Meiyu Season Over the Yangtze-Huaihe River Valley in China.

In 2020, the Yangtze-Huai river valley (YHRV) experienced the highest record-breaking Meiyu season since 1961, which was mainly characterized by the longest duration of precipitation lasting from early-June to mid-July, with frequent heavy rainstorms that caused severe flooding and deaths in China. Many studies have investigated the causes of this Meiyu season and its evolution, but the accuracy of precipitation simulations has received little attention. It is important to provide more accurate precipitation forecasts to help prevent and reduce flood disasters, thereby facilitating the maintenance of a healthy and sustainable earth ecosystem. In this study, we determined the optimal scheme among seven land surface model (LSMs) schemes in the Weather Research and Forecasting model for simulating the precipitation in the Meiyu season during 2020 over the YHRV region. We also investigated the mechanisms in the different LSMs that might affect precipitation simulations in terms of water and energy cycling. The results showed that the simulated amounts of precipitation were higher under all LSMs than the observations. The main differences occurred in rainstorm areas (>12mm/day), and the differences in low rainfall areas were not significant (<8mm/day). Among all of the LSMs, the Simplified Simple Biosphere (SSiB) model obtained the best performance, with the lowest root mean square error and the highest correlation. The SSiB model even outperformed the Bayesian model averaging result. Finally, some factors responsible for the differences modeling results were investigated to understand the related physical mechanism.

Performance evaluation of the WRF model in a tropical region: wind speed analysis at different sites

In this study, the performance of the mesoscale Weather Research and Forecasting (WRF) model is evaluated using combinations of three Planetary Boundary Layer (PBL) and three Land Surface Model (LSM) schemes, in order to identify the optimal parameters for the determination of wind speed in a tropical region. The state of Bahia in Brazil is selected as the location for the case study and simulations are performed over a period of eight months between 2015 and 2016. This is done to ensure that the dry and rainy seasons at the three different experimental sites—Esplanada, Mucuri, and Mucugê—are well separated from each other. The results of the simulations are compared with the observational data obtained from three towers equipped with anemometers at heights of 80, 100, 120 and 150 m, strategically placed at each site. Overestimation of wind speed is observed in the simulations, despite similarities between the simulated and observed wind directions. In addition, the accuracies of simulations corresponding to sites that are closer to the ocean are observed to be lower—the most accurate wind speed estimates are obtained corresponding to Mucugê, which is located farthest from the ocean. Finally, analysis of the results obtained from each tower accounting for periods with higher and lower precipitation reveals that the combination of the PBL-YSU scheme with the LSM-RUC scheme yields the best results.

Simulation analysis of local land atmosphere coupling in rainy season over a typical underlying surface in the Tibetan Plateau

Abstract. The local land–atmosphere coupling (LoCo) investigates the interactions between soil conditions, surface fluxes, planetary boundary layer (PBL) growth, and the formations of convective clouds and precipitation. Studying LoCo over the Tibetan Plateau (TP) is of great significance for understanding the TP's role in the Asian water tower. A series of real-case simulations, using the Weather Research and Forecasting (WRF) model with different combinations of land surface model (LSM) schemes and PBL schemes, has been carried out to investigate the LoCo characteristics over a typical underlying surface in the central TP in the rainy season. The LoCo characteristics in the study area are analyzed by applying a mixing diagram to the simulation results. The analysis indicates that the WRF simulations, using the Noah with BouLac, Mellor-Yamada Nakanishi and Niino Level-2.5 PBL (MYNN), and Yonsei University (YSU) produce closer results to the observation in terms of curves of Cp⋅θ and Lv⋅q, surface fluxes (Hsfc and LEsfc), entrainment fluxes (Hent, and LEent) at site BJ of Nagqu Station (BJ/Nagqu) than those using the Community Land Model (CLM) with BouLac, MYNN, and YSU. The frequency distributions of Hsfc, LEsfc, Hent, and LEent in the study area confirm this result. The spatial distributions of simulated Hsfc, LEsfc, Hent, and LEent, using WRF with Noah and BouLac, suggest that the spatial distributions of Hsfc and LEsfc in the study area are consistent with that of soil moisture, but the spatial distributions of Hent and LEent are quite different from that of soil moisture. A close examination of the relationship between entrainment fluxes and cloud water content (QCloud) reveals that the grids with small Hent and large LEent tend to have high QCloud and Hsfc, suggesting that high Hsfc is conducive to convective cloud formation, which leads to small Hent and large LEent. A sensitivity analysis of LoCo to the soil moisture at site BJ/Nagqu indicates that, on a sunny day, an increase in soil moisture leads to an increase in LEsfc but decreases in Hsfc, Hent, and LEent. The sensitivity of the relationship between simulated maximum daytime PBL height (PBLH) and mean daytime evapotranspiration (ET) in the study area to soil moisture indicates the rate at which the maximum daytime PBLH decreases with the mean ET increase as the initial soil moisture goes up. The analysis of simulated Hsfc, LEsfc, Hent, and LEent under different soil moisture conditions reveals that the frequency of Hent ranging from 80 to 240 W m−2 and the frequency of LEent ranging from −240 to −90 W m−2 both increase as the initial soil moisture increases. Coupled with the changes in QCloud, the changes in Hent and LEent as the initial soil moisture increases indicate that the rise in soil moisture leads to an increase in the cloud amount but a decrease in QCloud.

Comparison of Urban Heat Island Intensity Estimation Methods Using Urbanized WRF in Berlin, Germany

In this study, we present a meso-scale simulation of the urban microclimate in Berlin, Germany, using the Weather Research and Forecasting (WRF) numerical weather prediction platform. The objective of the study is to derive an accurate estimate of the near-surface urban heat island (UHI) intensity. The simulation is conducted over a two-week summer period. We compare different physical schemes, different urban canopy schemes and different methods for estimating the UHI intensity. The urban fraction of each urban category is derived using the Copernicus Impervious Density data and the Corine Land Cover data. High-resolution City Geography Markup Language (CityGML) data is used to estimate the building height densities required by the multi-layer urban canopy model (UCM). Within the single-layer UCM, we implement an anthropogenic heat profile based on the large scale urban consumption of energy (LUCY) model. The optimal model configuration combines the WRF Single Moment Five-Class (WSM5) microphysics scheme, the Bougeault–Lacarrère planetary boundary layer scheme, the eta similarity (Mellor–Yamada–Janjic) surface layer scheme, the Noah Multi-Parameterization land surface model, the Dudhia and Rapid Radiative Transfer Model (RRTM) radiation schemes, and the multi-layer UCM (including the building energy model). Our simulated UHI intensity results agree well with measurements with a root mean squared error of 0.86K and a mean bias error of 0.20K. After model validation, we proceed to compare several UHI intensity calculation methods, including the ‘ring rural reference’ (RRR) method and the ‘virtual rural reference’ (VRR) method. The VRR mthod is also known as the ‘urban increment’ method. We suggest and argument that the VRR approach is superior.

WRF‐based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis

AbstractThe High Asia Refined analysis (HAR) is a regional atmospheric data set generated by dynamical downscaling of the Final operational global analysis (FNL) using the Weather Research and Forecasting (WRF) model. It has been successfully and widely utilized. A new version (HAR v2) with longer temporal coverage and extended domains is currently under development. ERA5 reanalysis data is used as forcing data. This study aims to find the optimal set‐up for the production of the HAR v2 to provide similar or even better accuracy as the HAR. First, we conducted a sensitivity study, in which different cumulus, microphysics, planetary boundary layer, and land surface model schemes were compared and validated against in situ observations. The technique for order preference by similarity to the ideal solution (TOPSIS) method was applied to identify the best schemes. Snow depth in ERA5 is overestimated in High Mountain Asia (HMA) and causes a cold bias in the WRF output. Therefore, we used Japanese 55‐year Reanalysis (JRA‐55) to correct snow depth initialized from ERA5 based on the linear scaling approach. After applying the best schemes identified by the TOPSIS method and correcting the initial snow depth, the model performance improves. Finally, we applied the improved set‐up for the HAR v2 and computed a one‐year run for 2011. Compared to the HAR, the HAR v2 has a better representation of air temperature at 2 m. It produces slightly higher precipitation amounts, but the spatial distribution of seasonal mean precipitation is closer to observations.

Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application

Abstract. This study assesses the usability of Weather Research and Forecasting (WRF) model simulated soil moisture for landslide monitoring in the Emilia Romagna region, northern Italy, during the 10-year period between 2006 and 2015. In particular, three advanced land surface model (LSM) schemes (i.e. Noah, Noah-MP, and CLM4) integrated with the WRF are used to provide detailed multi-layer soil moisture information. Through the temporal evaluation with the single-point in situ soil moisture observations, Noah-MP is the only scheme that is able to simulate the large soil drying phenomenon close to the observations during the dry season, and it also has the highest correlation coefficient and the lowest RMSE at most soil layers. It is also demonstrated that a single soil moisture sensor located in a plain area has a high correlation with a significant proportion of the study area (even in the mountainous region 141 km away, based on the WRF-simulated spatial soil moisture information). The evaluation of the WRF rainfall estimation shows there is no distinct difference among the three LSMs, and their performances are in line with a published study for the central USA. Each simulated soil moisture product from the three LSM schemes is then used to build a landslide prediction model, and within each model, 17 different exceedance probability levels from 1 % to 50 % are adopted to determine the optimal threshold scenario (in total there are 612 scenarios). Slope degree information is also used to separate the study region into different groups. The threshold evaluation performance is based on the landslide forecasting accuracy using 45 selected rainfall events between 2014 and 2015. Contingency tables, statistical indicators, and receiver operating characteristic analysis for different threshold scenarios are explored. The results have shown that, for landslide monitoring, Noah-MP at the surface soil layer with 30 % exceedance probability provides the best landslide monitoring performance, with its hit rate at 0.769 and its false alarm rate at 0.289.

Prediction of heavy rainfall over Chennai Metropolitan City, Tamil Nadu, India: Impact of microphysical parameterization schemes

This study attempts to investigate the real-time prediction of a heavy rainfall event over the Chennai Metropolitan City, Tamil Nadu, India that occurred on 01 December 2015 using Advanced Research Weather Research and Forecasting (WRF-ARW) model. The study evaluates the impact of six microphysical (Lin, WSM6, Goddard, Thompson, Morrison and WDM6) parameterization schemes of the model on prediction of heavy rainfall event. In addition, model sensitivity has also been evaluated with six Planetary Boundary Layer (PBL) and two Land Surface Model (LSM) schemes. Model forecast was carried out using nested domain and the impact of model horizontal grid resolutions were assessed at 9km, 6km and 3km. Analysis of the synoptic features using National Center for Environmental Prediction Global Forecast System (NCEP-GFS) analysis data revealed strong upper-level divergence and high moisture content at lower level were favorable for the occurrence of heavy rainfall event over the northeast coast of Tamil Nadu. The study signified that forecasted rainfall was more sensitive to the microphysics and PBL schemes compared to the LSM schemes. The model provided better forecast of the heavy rainfall event using the logical combination of Goddard microphysics, YSU PBL and Noah LSM schemes, and it was mostly attributed to timely initiation and development of the convective system. The forecast with different horizontal resolutions using cumulus parameterization indicated that the rainfall prediction was not well represented at 9km and 6km. The forecast with 3km horizontal resolution provided better prediction in terms of timely initiation and development of the event. The study highlights that forecast of heavy rainfall events using a high-resolution mesoscale model with suitable representations of physical parameterization schemes are useful for disaster management and planning to minimize the potential loss of life and property.

Impacts of spectral nudging on the simulated surface air temperature in summer compared with the selection of shortwave radiation and land surface model physics parameterization in a high-resolution regional atmospheric model

The impact of a spectral nudging technique for the dynamical downscaling of the summer surface air temperature in a high-resolution regional atmospheric model is assessed. The performance of this technique is measured by comparing 16 analysis-driven simulation sets of physical parameterization combinations of two shortwave radiation and four land surface model schemes of the model, which are known to be crucial for the simulation of the surface air temperature. It is found that the application of spectral nudging to the outermost domain has a greater impact on the regional climate than any combination of shortwave radiation and land surface model physics schemes. The optimal choice of two model physics parameterizations is helpful for obtaining more realistic spatiotemporal distributions of land surface variables such as the surface air temperature, precipitation, and surface fluxes. However, employing spectral nudging adds more value to the results; the improvement is greater than using sophisticated shortwave radiation and land surface model physical parameterizations. This result indicates that spectral nudging applied to the outermost domain provides a more accurate lateral boundary condition to the innermost domain when forced by analysis data by securing the consistency with large-scale forcing over a regional domain. This consequently indirectly helps two physical parameterizations to produce small-scale features closer to the observed values, leading to a better representation of the surface air temperature in a high-resolution downscaled climate.

Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting

This paper proposes an unsupervised tumour segmentation approach for PET data. The method computes the volumes of interest (VOIs) with sub-voxel precision by considering the limited image resolution and partial volume effects. First, an improved anisotropic diffusion filter is used to remove image noise. A hierarchical local and global intensity active surface modelling scheme is then applied to segment VOIs, followed by an alpha matting step to further refine the segmentation boundary. The proposed method is validated on real PET images of head-and-neck cancer patients with ground truth provided by human experts, as well as custom-designed phantom PET images with objective ground truth. Experimental results show that our method outperforms previous automatic approaches in terms of segmentation accuracy.

A surface modeling method based on the envelope template

In this paper, we present a novel surface modeling scheme based on an envelope template. A two-parameter family of interpolating surfaces is generated by repeated bicubic interpolation of the given data points, and then a solution to the envelope condition and the envelope of the family are constructed. The continuity conditions of two adjacent patches along the common boundary are derived by analyzing the geometric properties of the envelope patch. In order to facilitate surface modeling, an envelope template is constructed, which has many desirable advantages including simple structure, good local features and so on. G 2 or C 2 composite surfaces can be obtained utilizing the envelope template sweeping over the data points.

Subdivision surface modeling for spiral bevel gear manufacturing

This paper presents a study on spiral bevel gear (SBGs) manufacturing method based on subdivision surface modeling scheme. The explicit nominal surface descriptions for both gears and pinions have been first developed based on gear generative motions. Then a group of key points on the tooth surface are selected as seed points (starting points) for a subdivision surface. From a given manufacturing precision criteria, a subdivision surface can be generated with a limited number of iterations during a subdivision surface modeling. This new surface modeling technique can be used for manufacturing (SBGs) with a general numerical control machine and a tool cutter in a flexible manufacturing environment. The experimental evaluation has been carried out and indicated that the subdivision surface-based SBG manufacturing method has advantages in high precise SBG manufacturing and precision control.

Towards an integrated inversion of geoscientific data: What price of geology?

The inversion of multiple geoscientific datasets to define structures in hard rock terrains has for the most part been limited to geophysical methods supported by rock property constraints. In this paper we expand the range of constraints available to geological modellers by defining a series of misfit functions that are used to define the quality of a given geological model with respect to the available geological data. These misfit functions encompass both spatial and temporal observations, and include primary data such as the location of an outcrop or bore hole location of a given rock type, the orientation of its different generations of foliations, as well as secondary geologic observations such as age relationships. When we combine geologic conditions with geophysical misfit functions, which already exist for many geophysical measures, we have the potential to better constrain two- and 3D models of the Earth. As a first test, a subset of these misfit functions are applied to a set of synthetic three-dimensional volumetric models built with an implicit surface modelling scheme. By perturbing the input structural data within a narrow range, we can simulate a range of significantly different three-dimensional models, for which we can then apply both geological and geophysical misfit functions. Several joint geological/geophysical inversion schemes may be developed based on this methodology, with the ultimate goal being an inversion scheme that simultaneously minimizes both geological and geophysical misfit.

Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics

The fully coupled WRF/Chem (Weather Research and Forecasting/Chemistry) model is used to simulate air quality in the Mississippi Gulf coastal region at a high resolution (4 km) for a moderately severe summer ozone episode between 18 CST 7 and 18 CST 10 June 2006. The model sensitivity is studied for meteorological and gaseous criteria pollutants (O3, NO2) using three Planetary Boundary Layer (PBL) and four land surface model (LSM) schemes and comparison of model results with monitoring station observations. Results indicated that a few combinations of PBL and LSMs could reasonably produce realistic meteorological fields and that the combination of Yonsei University (YSU) PBL and NOAH LSM provides best predictions for winds, temperature, humidity and mixed layer depth in the study region for the period of study. The diurnal range in ozone concentration is better estimated by the YSU PBL in association with either 5‐layer or NOAH land surface model. The model seems to underestimate the ozone concentrations in the study domain because of underestimation of temperatures and overestimation of winds. The underestimation of NO2 by model suggests the necessity of examining the emission data in respect of its accurate representation at model resolution. Quantitative analysis for most monitoring stations indicates that the combination of YSU PBL with NOAH LSM provides the best results for various chemical species with minimum BIAS, RMSE, and high correlation values.

Impacts of boundary layer turbulence and land surface process parameterizations on simulated sea breeze characteristics

Abstract. This paper investigates the sensitivity of sea breeze (SB) simulations to combinations of boundary-layer turbulence and land-surface process parameterizations implemented in the MM5 mesoscale meteorological mode for an observed SB case over the Swedish west coast. Various combinations from four different planetary boundary layer (PBL) schemes [Blackadar, Gayno-Seaman (GS), Eta, MRF], and two land surface model (LSM) schemes (SLAB, Noah) with different complexity are designed to simulate a typical SB case over the Swedish west coast. The simulations are conducted using two-way interactively nested grids. Simulated 10-m winds are compared against observed near-surface wind data from the GÖTE2001 campaign to examine the diurnal cycle of wind direction and speed for SB timing. The SB (vertical) circulation is also compared in the different experiments. The results show that the different combinations of PBL and LSM parameterization schemes result in different SB timing and vertical circulation characteristics. All experiments predict a delayed SB. The vertical component of the SB circulation varies in the experiments, among which the GS PBL scheme produces the strongest SB circulation. Evident differences between the SLAB and Noah LSMs are also found, especially in maximum of updraft and downdraft velocities of the SB vertical circulation. The results have significant implications for convective initiation, air quality studies and other environmental problems in coastal areas.

Design of propeller geometry using streamline-adapted blade sections

We refine the geometry of a propeller by modifying the blade sections to align them with surface streamlines, obtained by the panel method. Redefinition of the blade sections aligned with the streamlines is provided together with surface modeling scheme by which model propellers were built. Numerical simulations and open-water tests on models suggest a possible increase of 1% in propeller efficiency.

Deformation Modeling for Robust 3D Face Matching

Face recognition based on 3D surface matching is promising for overcoming some of the limitations of current 2D image-based face recognition systems. The 3D shape is generally invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates to account for various expressions for each subject in a large database is not practical. We propose a facial surface modeling and matching scheme to match 2.5D facial scans in the presence of both non-rigid deformations and pose changes (multiview) to a 3D face template. A hierarchical geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A user-specific (3D) deformable model is built by combining the templates with synthesized deformations. The matching distance is computed by fitting this generative deformable model to a test scan. A fully automatic and prototypic 3D face matching system has been developed. Experimental results demonstrate that the proposed deformation modeling scheme increases the 3D face matching accuracy.

Process monitoring in stamping operations through tooling integrated sensing

This paper introduces a new sensing method for stamping process monitoring based on the measurement of contact pressure distribution across the sheet metal-tooling interface, by means of an array of tooling-integrated force sensors. The role of numeric surface methods in estimating the contact pressure distribution on the sheet metal-tooling interface has been studied through finite element analysis and experiments. A finite element model is set up to model the contact interactions, based on the geometry of a customized stamping test fixture. Discrete samples of contact pressure taken from the FE model have been used to recreate continuous-pressure surfaces based on the Thin Plate Splines (TPS) and Bezier surface algorithms. It is shown that the temporal–spatial contact pressure distribution across the sheet metal-tooling interface can be effectively reconstructed through interpolation using spatially discrete sensor data. Comparison of surface-based pressure estimates with the FEA field solution indicates that the TPS-based method is more accurate than the Bezier method. The effectiveness of the surface modeling scheme is also evaluated experimentally by comparing the net press force calculated from numerical integration of the TPS surfaces with the experimentally measured value under different press speeds. The effectiveness of the new sensing method is further demonstrated by detection of slide misparallelism through analysis of the tooling interface pressure distribution. The study presents a new approach to enhancing process observability in manufacturing operations.