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Mineral aerosols (i.e. dust) perturb the Earth’s energy budget by scattering and absorbing shortwave (SW) and longwave (LW) radiation. Generally dust has a net (SW + LW) negative direct radiative effect (DRE) at the surface and top of the atmosphere (i.e. cooling of the Earth’s climate system). A few studies found that the net top of the atmosphere (TOA) DRE of dust, the difference between the net radiative flux in clear-sky (cloud-free) and pristine-sky (aerosol and cloud free) conditions, can be near zero or positive over desert regions. It is important to constrain the net DRE of dust over source regions since biases in this parameter can lead to errors in estimates of the global average. In Chapter 2 an observation-based method to estimate the dust SW forcing efficiency (FE), the DRE normalized by the aerosol optical depth, is developed and validated. Associated uncertainties in this method are further investigated. By accounting for the relationship between dust and water vapor, biases in the TOA SW FE of dust are reduced and estimates of the TOA SW F E of dust over the Sahara Desert range from −26.4±17.6 to 2.35±139 W m^-2 per optical depth. In Chapter 3 this method is applied to observations and retrievals from a small dust source region in southeastern California to estimate the surface and TOA dust SW FE. These results are compared with the dust SW FE estimated with the output from a radiative transfer model. After validating the model, modeled and observed estimates of the instantaneous SW FE are statistically similar and therefore the model is used to estimate the instantaneous dust LW FE and the diurnally averaged net dust FE. Values of the net FE of dust are −29±10, −10±11, and 19±15 W m^-2 per optical depth at the surface, TOA, and atmosphere, respectively, over the small dust source region. The findings presented in this dissertation contribute towards the breadth of observational estimates of the SW dust FE over dust source regions which is critical to constrain model-based estimates of regional and global values of the dust SW FE.

The medium-resolution spectral imager (MERSI) onboard China's new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) is providing global observations of the Earth's environment. One of the important characteristics of the MERSI sensor is that it has four bands at 250-m spatial resolution. However, very few high-level land surface and atmospheric products have been generated from MERSI data. This article presents an effective optimization method to estimate the continuous temporal distribution of multiple land surface and atmospheric variables at a 250-m spatial resolution from time series FY-3B MERSI top-of-atmosphere (TOA) observations. The MODIS cloud detection method was applied to the MERSI TOA reflectance to determine the clear observations. Then, a physics-based BRDF correction was adopt to reduce the topographic effects using the slope angle and aspect angle of the slope. Finally, a coupled land surface-atmosphere radiative transfer (RT) model and the shuffled complex evolution (SCE) optimization algorithm were used to estimate a suit of variables, including the leaf area index (LAI), the aerosol optical depth (AOD), the cloud optical thickness (COT), the cloud effective particle radius (CER), the land surface reflectance, the shortwave and visible albedo, the incident shortwave radiation (ISR), the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR (FAPAR), the surface broadband emissivity (BBE), and the TOA shortwave albedo. The method was applied to the data from the 18 SURFRAD, the America FluxNet (AmeriFlux), and the Images and Beijing Normal University net (BNUnet) sites. All the estimated variables were also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR were also validated using ground measurements from the selected sites. The results show that our method can simultaneously estimate multiple temporally continuous variables in both clear- and cloudy-sky conditions, with an accuracy comparable to those of MODIS and GLASS products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR are consistent with the field measurements, with coefficients of determination (R²) of 0.692, 0.783, 0.788, 0.559, and 0.833, and root mean square errors (RMSEs) of 0.427, 56.681 W/m², 0.092, 0.084, and 115.305 W/m², respectively. The proposed algorithm can effectively estimate the instantaneous atmospheric and land surface variables directly from satellite observations without cumbersome atmospheric corrections. Moreover, it can potentially be applied to multiple satellite observations.

Solar greenhouses are currently the most energy-intensive agricultural sector. In literature, there is no worldwide mapping of solar greenhouse performance under different climate scenarios. This study analyzes the performance of a Venlo solar greenhouse for 48 localities around the world. Solar greenhouses are mainly made of a transparent envelope and the effect of the direct and diffuse component of solar radiation impacts the internal plant well-being. This study aims to identify the best solution of a transparent envelope on locations with different latitudes and evenly distributed around the globe. The simulations are carried out using TRNsys, considering different thermal phenomena three-dimensional shortwave and longwave radiative exchange, airflow exchanges, presence of lamps with their exact 3D position, ground, plant evapotranspiration, and convective heat transfer coefficients. A total of 336 simulations are performed in the free-floating regime. A new index for the identification of the best glass solutions based on annual average deviation is defined. For all climates, the best glass solutions work better in winter than in summer. The optimal choice of the glass must be combined with effective scheduling of openings for natural ventilation to avoid internal overheating phenomena.

The Normalization Difference Vegetation Index (NDVI), for many years, was widely used in remote sensing for the detection of vegetation land cover. This index uses red channel radiances (i.e., 0.66 μm reflectance) and near-IR channel (i.e., 0.86 μm reflectance). In the heavy chlorophyll absorption area, the red channel is located, while in the high reflectance plateau of vegetation canopies, the Near-IR channel is situated. Senses of channels (Red & Near- IR) read variance depths over vegetation canopies. In the present study, a further index for vegetation identification is proposed. The normalized difference vegetation shortwave index (NDVSI) is defined as the difference between the cubic bands of Near- IR and Shortwave infrared radiation (SWIR) divided by their sums. The radiances or reflectances are included in this index from the Near-IR channel and WSIR2 channel (2.1 μm). The NDVSI is less sensitivite to atmospheric effects as compared to NDVI. By comparing the one NDVSI index with the two indexes (NDVI, SAVI) of vegetation cover, good correlations were found between NDVI and NDVSI (R2=0.917) and between SAVI and NDVSI (R2=0.809. Accordingly, the proposed index can be taken into consideration as an independent vegetation index

The Western Tibetan Vortex (WTV) is a large-scale circulation pattern identified from year-to-year circulation variability, which was used to understand the causal mechanisms for slowdown of the glacier melting over the western Tibetan Plateau (TP). A recent argument has suggested the WTV is the set of wind field anomalies resulting from variability in near-surface air temperatures over the western TP (above 1500 m), which, in turn, is likely driven by the surface net radiation. This study thereby evaluates the above putative thermal-direct mechanism. By conducting numerical sensitivity experiments using a global atmospheric circulation model, SAMIL, we find a WTV-like structure cannot be generated from a surface thermal forcing imposed on the western TP. A thermally-direct circulation generated by the surface or near surface heating is expect to cause upward motions and a baroclinic structure above it. In contrast, downward motions and a quasi-barotropic are observed in the vertical structure of the WTV. Besides, we find variability of the surface net radiation (sum of the surface shortwave and longwave net radiation) over the western TP can be traced back to the WTV variability based on ERA5 data. The anticyclonic (cyclonic) WTV reduces (increases) the cloudiness through the anomalous downward (upward) motions, causes more (less) input shortwave net radiation and thereby more (less) surface net radiations, resulting in the warmer (cooler) surface and near-surface air temperature over the western TP. The argument is constructive in encouraging examination of the radiative balance processes that complements previous studies.

Mineral dust aerosols cool and warm the atmosphere by scattering and absorbing solar (shortwave: SW) and thermal (longwave: LW) radiation. However, significant uncertainties remain in dust radiative effects, largely due to differences in the dust size distribution and spectral optical properties simulated in Earth system models. Dust models typically underestimate the coarse dust load (more than 2.5 µm in diameter) and assume a spherical shape, which leads to an overestimate of the fine dust load (less than 2.5 µm) after the dust emissions in the models are scaled to match observed dust aerosol optical depth at 550 nm (DAOD550). Here, we improve the simulated dust properties with data sets that leverage measurements of size-resolved dust concentration, asphericity factor, and refractive index in a coupled global chemical transport model with a radiative transfer module. After the adjustment of size-resolved dust concentration and spectral optical properties, the global and annual average of DAOD550 from the simulation increases from 0.023 to 0.029 and falls within the range of a semi-observationally based estimate (0.030 ± 0.005). The reduction of fine dust load after the adjustment leads to a reduction of the SW cooling at the top of the atmosphere (TOA). To improve agreement against a semi-observationally based estimate of the radiative effect efficiency at TOA, we find that a less absorptive SW dust refractive index is required for coarser aspherical dust. Thus, only a minor difference is estimated for the net global dust radiative effect at TOA (−0.08 vs. −0.00 W m−2 on a global scale). Conversely, our sensitivity simulations reveal that the surface warming is substantially enhanced near the strong dust source regions (less cooling to −0.23 from −0.60 W m−2 on a global scale). Thus, less atmospheric radiative heating is estimated near the major source regions (less heating to 0.15 from 0.59 W m−2 on a global scale), because of enhanced LW warming at the surface by the synergy of coarser size and aspherical shape.

Clouds cover on average nearly 70% of Earth’s surface and regulate the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and three-dimensional (3D) structure. Due to computational limitations, Earth system models are unable to perform 3D radiative transfer calculations. Instead they make assumptions, including the independent column approximation (ICA), that neglect effects of 3D cloud morphology on albedo. We show how the resulting radiative flux bias (ICA-3D) depends on cloud morphology and solar zenith angle. We use high-resolution (20–100-m horizontal resolution) large-eddy simulations to produce realistic 3D cloud fields covering three dominant regimes of low-latitude clouds: shallow cumulus, marine stratocumulus, and deep convective cumulonimbus. A Monte Carlo code is used to run 3D and ICA broadband radiative transfer calculations; we calculate the top-of-atmosphere (TOA) reflected flux and surface irradiance biases as functions of solar zenith angle for these three cloud regimes. Finally, we use satellite observations of cloud water path (CWP) climatology, and the robust correlation between CWP and TOA flux bias in our LES sample, to roughly estimate the impact of neglecting 3D cloud radiative effects on a global scale. We find that the flux bias is largest at small zenith angles and for deeper clouds, while the albedo bias is most prominent for large zenith angles. In the tropics, the annual-mean shortwave radiative flux bias is estimated to be 3.1 ± 1.6 W m−2, reaching as much as 6.5 W m−2 locally.

Understanding the regional surface temperature responses to different anthropogenic climate forcing agents, such as greenhouse gases and aerosols, is crucial for understanding past and future regional climate changes. In modern climate models, the regional temperature responses vary greatly for all major forcing agents, but the causes of this variability are poorly understood. Here, we analyze how changes in atmospheric and oceanic energy fluxes due to perturbations in different anthropogenic climate forcing agents lead to changes in global and regional surface temperatures. We use climate model data on idealized perturbations in four major anthropogenic climate forcing agents (CO2, CH4, sulfate, and black carbon aerosols) from Precipitation Driver Response Model Intercomparison Project (PDRMIP) climate experiments for six climate models (CanESM2, HadGEM2-ES, NCAR-CESM1-CAM4, NorESM1, MIROC-SPRINTARS, GISS-E2). Particularly, we decompose the regional energy budget contributions to the surface temperature responses due to changes in longwave and shortwave fluxes under clear-sky and cloudy conditions, surface albedo changes, and oceanic and atmospheric energy transport. We also analyze the regional model-to-model temperature response spread due to each of these components. The global surface temperature response stems from changes in longwave emissivity for greenhouse gases (CO2 and CH4) and mainly from changes in shortwave clear-sky fluxes for aerosols (sulfate and black carbon). The global surface temperature response normalized by effective radiative forcing is nearly the same for all forcing agents (0.63, 0.54, 0.57, 0.61 K W−1 m2). While the main physical processes driving global temperature responses vary between forcing agents, for all forcing agents the model-to-model spread in temperature responses is dominated by differences in modeled changes in longwave clear-sky emissivity. Furthermore, in polar regions for all forcing agents the differences in surface albedo change is a key contributor to temperature responses and its spread. For black carbon, the modeled differences in temperature response due to shortwave clear-sky radiation are also important in the Arctic. Regional model-to-model differences due to changes in shortwave and longwave cloud radiative effect strongly modulate each other. For aerosols, clouds play a major role in the model spread of regional surface temperature responses. In regions with strong aerosol forcing, the model-to-model differences arise from shortwave clear-sky responses and are strongly modulated by combined temperature responses to oceanic and atmospheric heat transport in the models.

On a global and annual average, we find a parameterization in which the cloud cover increase is proportional to the mid tropospheric temperature increase, with a negative proportionality factor. If the relative humidity is conserved throughout the troposphere, a 1 °C heating (cooling) of the mid troposphere, decreases (increases) the cloud cover by 1.5 percentage points (pp). But if the relative humidity is not conserved, then the cloud cover decreases (increases) by 7.6 pp. If the shortwave reflection effect of the cloud cover is dominant on a global scale, this parameterization leads to a predominant positive feedback: if the temperature increases like in the current climate change, the cloud cover decreases and more solar radiation reaches the surface increasing the temperature even more. The contribution of the present work consists in finding that the negative sign of the proportionality factor is due to the Clausius–Clapeyron equation; that is, to the magnitude of the derivative of the saturation vapor pressure at the typical standard surface temperature of 288 K. The negative sign of the factor is independent on the conservation or non-conservation of relative humidity in the troposphere under climate change.

Local short-term temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy balance terms, which are well known but whose specific contribution and importance on the hourly scale still need to be further analyzed. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective being to estimate their importance in hourly near-surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloudy-sky radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycle representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear-sky radiation for 1 h temperature variations during the day and mainly drive these 1 h temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects near-surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.

This work highlights the potential significance of palaeoclimate model outputs in explaining the physical processes and mechanisms to better understand the speleothem-based proxy records of the precipitation and temperature signals during the Indian northeast and southwest monsoon from 850 to 2000AD. Thus, discussion is mainly focused on this conception by providing insights for aligning modeling preferences with the proxy records. δ18O speleothem values show that precipitation over northeast India (NEI) remained constantly higher than that over the summer core monsoon zone (CMZ) from 850-2000AD. Based on the dynamical numerical coupled model (CSIRO-Mk3L-1–2) outputs, the data are in alignment with the presence of four proxy-based major extreme climatic events: Dry Century (DC), Medieval Climatic Anomaly (MCA), Extreme Dry Century (EDC) and Little Ice Age (LIA) during the last millennia. In these extremes a significant trend in diurnal temperature range (DTR), in particular, is correspondingly observed. These extremes and associated mechanisms are examined using surface energetics and corresponding atmospheric-oceanic coupled processes. The analysis of downward radiative (shortwave and longwave), latent and sensible heat fluxes provided better rationale and understanding of associated physical processes and mechanisms. It is found that the latent and sensible heat fluxes were balanced by downward radiative fluxes and the remaining part of energy was anomalously trapped inside the surface, thus reflecting anomalous climatic behavior during these extremes.

Extensive regions of marine boundary layer cloud impact the radiative balance through their significant shortwave albedo while having little impact on outgoing longwave radiation. Despite this importance, these cloud systems remain poorly represented in large-scale models due to difficulty in representing the processes that drive their life cycle and coverage. In particular, the mesoscale organization and cellular structure of marine boundary clouds have important implications for the subsequent cloud feedbacks. In this study, we use long-term (2013–2018) observations from the Atmospheric Radiation Measurement (ARM) Facility's Eastern North Atlantic (ENA) site on Graciosa Island, Azores, Portugal, to identify cloud cases with open- or closed-cellular organization. More than 500 h of each organization type are identified. The ARM observations are combined with reanalysis and satellite products to quantify the cloud, precipitation, aerosol, thermodynamic, and large-scale synoptic characteristics associated with these cloud types. Our analysis shows that both cloud organization populations occur during similar sea surface temperature conditions, but the open-cell cases are distinguished by stronger cold-air advection and large-scale subsidence compared to the closed-cell cases, consistent with their formation during cold-air outbreaks. We also find that the open-cell cases were associated with deeper boundary layers, stronger low-level winds, and higher rain rates compared to their closed-cell counterparts. Finally, raindrops with diameters larger than 1 mm were routinely recorded at the surface during both populations, with a higher number of large drops during the open-cellular cases. The similarities and differences noted herein provide important insights into the environmental and cloud characteristics during varying marine boundary layer cloud mesoscale organization and will be useful for the evaluation of model simulations for ENA marine clouds.

Understanding historical wildfire variations and their environmental driving mechanisms is key to predicting and mitigating wildfires. However, current knowledge of climatic responses and regional contributions to the interannual variability (IAV) of global burned area remains limited. Using recent satellite-derived wildfire products and simulations from version v1.0 of the land component of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM land model [ELM] v1) driven by three different climate forcings, we investigated the burned area IAV and its climatic sensitivity globally and across nine biomes from 1997 to 2018. We found that 1) the ELM simulations generally agreed with the satellite observations in terms of the burned area IAV magnitudes, regional contributions, and covariations with climate factors, confirming the robustness of the ELM to the usage of different climate forcing sources; 2) tropical savannas, tropical forests, and semi-arid grasslands near deserts were primary contributors to the global burned area IAV, collectively accounting for 71.7%–99.7% of the global wildfire IAV estimated by both the satellite observations and ELM simulations; 3) precipitation was a major fire suppressing factor and dominated the global and regional burned area IAVs, and temperature and shortwave solar radiation were mostly positively related with burned area IAVs; and 4) noticeable local discrepancies between the ELM and remote-sensing results occurred in semi-arid grasslands, croplands, boreal forests, and wetlands, likely caused by uncertainties in the current ELM fire scheme and the imperfectly derived satellite observations. Our findings revealed the spatiotemporal diversity of wildfire variations, regional contributions and climatic responses, and provided new metrics for wildfire modeling, facilitating the wildfire prediction and management.

Based on a 16-warm-season statistical study on the mesoscale convective systems (MCSs) that were generated over the Tibetan Plateau (TP), 11 long-lived eastward propagating MCSs of the same type were selected for a composite semi-idealized simulation and a corresponding no-latent-heating sensitivity run by using the Weather Research and Forecasting (WRF) model. Common evolutionary features and associated mechanisms of this type of long-lived eastward propagating MCS were investigated. Main results are as follows: (i) This type of MCS was generated in a favorable background environment which was characterized by a notable upper-tropospheric divergence south of an upper-level jet, a strong warm advection around a middle-level shortwave trough’s central area, and an instable convective stratification below the trough. Development of the MCS featured rapid increase of cyclonic vorticity in the middle and lower troposphere. The convergence-related vertical stretching and tilting were key factors for the cyclonic-vorticity’s production, and convection-related upward cyclonic-vorticity transport contributed to the upward extending of the MCS. (ii) During the vacating stage of the MCS, it first coupled with a quasi-stationary Tibetan Plateau vortex (TPV) over the TP’s eastern section, and then decoupled from the vortex. In the former stage, the MCS contributed to maintaining ascending motions and convergence associated with the TPV, which favored its persistence; whereas, in the latter stage, decoupling weakened the TPV-associated convection significantly. This reduced the upward transport of cyclonic vorticity notably, which, together with the negative tilting effect, finally led to the vortex’s dissipation. (iii) After vacating TP, the MCS first weakened due to the disappearance of strong direct sensible heating from the TP on its bottom, and then, under the favorable conditions associated with the shortwave trough over the eastern section of the TP, the MCS redeveloped rapidly. Convergence-related cyclonic-vorticity production in the middle and lower troposphere and upward transport of cyclonic vorticity due to convection governed the MCS’s redevelopment. (iv) Sensitivity simulation shows that latent heating was a necessary condition for the formation and development of the long-lived eastward propagating MCS. On the one hand, this MCS affected the TP’s eastern section and downstream regions directly by inducing precipitation; and on the other hand, it exerted effects on the precipitation over a wider range in the downstream regions by modulating large-scale circulations over and around the TP.

The uncertainty in trend estimates caused by temporal scale effect has rarely been studied. In response to this challenge, this study aims to explore temporal scale effects on trend detection based on in situ measurements throughout China given that they provide full temporal details of surface elements. Three common temporal scales (daily, monthly, and yearly) were used to present findings. Two different trend detection methods (i.e., simple linear regression and breaks for additive seasonal and trend) were employed to check the dependence of temporal scale effects on methods. It was found that temporal scale effects were dependent on the type of elements, the time span of datasets, and trend detection methods. They are more significant for elements with fast changes (e.g., snow depth) compared to those with gradual changes (e.g., albedo, air temperature). And temporal scale effects from daily to monthly scale are generally larger than those from monthly to yearly scale. The former is almost independent of trend detection methods, while the latter shows a clear dependence (i.e., dealing with seasonality or not). Trend estimates of surface albedo and longwave net radiation are mainly affected by temporal scale effects, while those of snow depth, shortwave net radiation, and air temperature are affected by both temporal scale effects and the choice of trend detection methods. The feedbacks between these elements show a clear dependence on temporal scale, which are generally stronger on monthly scale but weaker on yearly scale.

Site adaptation refers to procedures for correcting systematic errors in an extended period of gridded modeled data using a short period of ground-based measurements used as unbiased reference. Traditionally, site adaptation leverages a single gridded product and issues point predictions. Currently, remote-sensed and reanalysis data are available from different sources providing multiple versions of estimates of a same atmospheric variable, for any location on Earth. These datasets allow what is called an ensemble prediction. In this regard, this contribution proposes a probabilistic site-adaption framework, and describes how one can use parametric and nonparametric techniques within the framework. On top of the stand-alone probabilistic site-adaption methods, heuristics are optionally used to combine quantiles, to further improve the accuracy of site adaptation. To exemplify the framework, global horizontal irradiance data from 26 sites worldwide with different climate characteristics and weather regimes are used to side-adapt the corresponding predictions from up to 5 satellite-derived databases and 2 reanalyses spanning various periods, collectively. It is found that the proposed site-adaptation methods using multiple gridded products are able to attain, on average, a 5 W/m 2 reduction in continuous ranked probability score than that leveraging just a single product.

The applications of high-spatial-resolution satellite data have been increasing in recent years owing to improvements in sensor techniques, and the errors in estimated parameters induced by ignoring topographic effects are increasingly stressed because their effects are important for parameter retrieval from high-spatial-resolution satellite observations. A coupled surface-atmosphere model is employed to develop a two-step multiparameter estimation scheme to simultaneously estimate multiple parameters (leaf area index, LAI; aerosol optical depth, AOD; photosynthetically active radiation, PAR; incident shortwave radiation, ISR; surface albedo, and fraction of absorbed photosynthetically active radiation, FAPAR) from long-term Landsat 4–8 top-of-atmosphere (TOA) observations. First, the influential parameters of the coupled model are retrieved through optimization retrieval strategies. Then, these estimated parameters are entered into the coupled model to compute the PAR, ISR, surface reflectance, surface albedo, and FAPAR. Validation of this scheme with in situ measurements from 57 sites demonstrates that it can successfully estimate multiple parameters from Landsat TOA data, with root mean square errors (RMSEs) of LAI, AOD, FAPAR, visible albedo, shortwave albedo, PAR, and ISR of 0.69, 0.16, 0.13, 0.034, 0.047, 26.80, and 64.28 W/m2, respectively. In the two-step multiparameter estimation scheme, atmospheric and topographic corrections of satellite observations are avoided because the atmospheric and topographic effects are incorporated, and the surface anisotropy is also effectively considered. In addition, by using the two-step multiparameter estimation scheme, physical connections among the multiple parameters are ensured since they are estimated from the same physical model.