Estimation Of Heat Flux Research Articles

Two-color pyrometry based flame to fuel surface radiative heat flux diagnostic using flamelets

In this study, a non-intrusive two-color pyrometry based radiative heat flux diagnostic is presented that accounts for the self-absorption effects. This diagnostic relies on looking up the flame temperature and soot volume fraction using the ratio of color intensities obtained from digital cameras and numerical solutions of 1D steady-state diffusion flames. Virtual two color pyrometry of computed flame show a unique one-to-one mapping of strained flames with intensity ratio; thereby allowing flames to be indexed and creation of a two-color flamelet manifold (TCFM). The TCFM is a function of two variables; the ratio of red to green intensities (flame index) and physical distance to the stoichiometric surface defined using a level-set function. The developed diagnostic is used in upward flame spread experiments where an approximate flame hull reconstruction method is applied to provide the 3D level-set function and intensity ratio for use of TCFM to provide radiative absorption properties for 3D ray tracing. Accounting for self-absorption effects provide improved soot volume fraction estimates. The radiative heat flux estimates are found to be in very good agreement with literature and improved results are obtained near the flame base.

A Quantitative Approach for Thermal Characterization of Phase Change Materials

Abstract A quantitative method for the calculation of phase change parameters of salt-based phase change materials (PCMs) has been proposed. This technique involves the estimation of mold-salt interfacial heat flux by solving Fourier’s law of heat conduction within the salt and using it for the calculation of phase change enthalpy of salt PCMs. Radial heat transfer was ensured by keeping the length to diameter (L/D) ratio of the mold equal to 5. The proposed method eliminates any drawbacks involved with sample size, reference material, the baseline fitting calculations, and the errors introduced due to the selection of solidification points. Pure salt PCMs such as potassium nitrate (KNO3), sodium nitrate (NaNO3), and solar salt mixture (60 wt. % NaNO3 + 40 wt. % KNO3) were used for validation of this technique. The thermal behaviors of the salt and the mold during solidification of the salt sample were analyzed, and solidification characteristics such as cooling rate, solidification time, and phase change enthalpy of PCMs were determined.

GBRT-Based Estimation of Terrestrial Latent Heat Flux in the Haihe River Basin from Satellite and Reanalysis Datasets

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB.

Estimation of the Latent Heat Flux over Irrigated Short Fescue Grass for Different Fetches

Often in agrometeorology the instrumentation required to estimate turbulent surface fluxes must be installed at sites where fetch is not sufficient for a sector of wind directions. For different integrated flux-footprints (IFFP) thresholds and taking as a reference the half-hourly latent heat fluxes (LE) measured with a large weighing lysimeter (LELys), the eddy covariance (EC) method and two methods based on surface renewal (SR) analysis to estimate LE were tested over short fescue grass. One method combined SR with the flux-gradient (profile) relationship, SR-P method, and the other with the dissipation method, SR-D method. When LE was estimated using traces of air moisture, good performances were obtained using the EC and the SR-P methods for samples with IFFP higher than 85%. However, the closest LE estimates were obtained using the residual method. For IFFP higher than 50%, the residual method combined with the sensible heat flux estimates determined using the SR-P method performed close to LELys and using the SR-D method good estimates were obtained for accumulated LELys. To estimate the sensible heat flux, the SR-D method can be recommended for day-to-day use by farmers because it is friendly and affordable.

Structure and drivers of ocean mixing north of Svalbard in summer and fall 2018

Abstract. The Arctic Ocean is a major sink for heat and salt for the global ocean. Ocean mixing contributes to this sink by mixing the Atlantic- and Pacific-origin waters with surrounding waters. We investigate the drivers of ocean mixing north of Svalbard, in the Atlantic sector of the Arctic, based on observations collected during two research cruises in summer and fall 2018. Estimates of vertical turbulent heat flux from the Atlantic Water layer up to the mixed layer reach 30 W m−2 in the core of the boundary current, and average to 8 W m−2, accounting for ∼1 % of the total heat loss of the Atlantic layer in the region. In the mixed layer, there is a nonlinear relation between the layer-integrated dissipation and wind energy input; convection was active at a few stations and was responsible for enhanced turbulence compared to what was expected from the wind stress alone. Summer melting of sea ice reduces the temperature, salinity and depth of the mixed layer and increases salt and buoyancy fluxes at the base of the mixed layer. Deeper in the water column and near the seabed, tidal forcing is a major source of turbulence: diapycnal diffusivity in the bottom 250 m of the water column is enhanced during strong tidal currents, reaching on average 10−3 m2 s−1. The average profile of diffusivity decays with distance from the seabed with an e-folding scale of 22 m compared to 18 m in conditions with weaker tidal currents. A nonlinear relation is inferred between the depth-integrated dissipation in the bottom 250 m of the water column and the tidally driven bottom drag and is used to estimate the bottom dissipation along the continental slope of the Eurasian Basin. Computation of an inverse Froude number suggests that nonlinear internal waves forced by the diurnal tidal currents (K1 constituent) can develop north of Svalbard and in the Laptev and Kara seas, with the potential to mix the entire water column vertically. Understanding the drivers of turbulence and the nonlinear pathways for the energy to turbulence in the Arctic Ocean will help improve the description and representation of the rapidly changing Arctic climate system.

Estimation of anthropogenic heat flux and its coupling analysis with urban building characteristics – A case study of typical cities in the Yangtze River Delta, China

The anthropogenic heat emitted into the atmosphere has increased significantly in urban areas with accumulated building and increased energy consumption. Carrying out anthropogenic heat flux (AHF) estimation and exploring the correlation between the AHF and building characteristics (density and height) helps reveal the urban climate's genetic mechanism. This study took Shanghai, Nanjing, and Hangzhou, the typical cities in the Yangtze River Delta region of China as case studies. The annual AHF of 2000, 2008, and 2016 based on the energy consumption inventory and multi-source remote sensing data was estimated. Besides, the monthly AHF by time dimension downscaling processing was obtained. The correlation between AHF and building characteristics was then analyzed by combining urban classification and building block data. The results showed that the AHF of typical cities increased significantly from 2000 to 2016, and the difference between the AHF of building area and the whole city was 12.69–20.36 W·m−2. Monthly AHF had a stratification phenomenon in different building characteristics. Moreover, building characteristics' impact on monthly AHF in the winter was more evident than that in the summer. The AHF of the building growth region was higher than that of the non-growth region, and the differences were more significant in the winter months (1.94–2.23 W·m−2) than in summer months (0.59–0.94 W·m−2). Building density on AHF was more significant than building height, and its contribution was higher than 80% on average in typical cities.

Estimations of Land Surface Characteristic Parameters and Turbulent Heat Fluxes over the Tibetan Plateau Based on FY-4A/AGRI Data

Accurate estimates of land surface characteristic parameters and turbulent heat fluxes play an important role in the understanding of land–atmosphere interaction. In this study, Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) satellite data and the China Land Data Assimilation System (CLDAS) meteorological forcing dataset CLDAS-V2.0 were applied for the retrieval of broadband albedo, land surface temperature (LST), radiation flux components, and turbulent heat fluxes over the Tibetan Plateau (TP). The FY-4A/AGRI and CLDAS-V2.0 data from 12 March 2018 to 30 April 2018 were first used to estimate the hourly turbulent heat fluxes over the TP. The time series data of in-situ measurements from the Tibetan Observation and Research Platform were divided into two halves—one for developing retrieval algorithms for broadband albedo and LST based on FY-4A, and the other for the cross validation. Results show the root-mean-square errors (RMSEs) of the FY-4A retrieved broadband albedo and LST were 0.0309 and 3.85 K, respectively, which verifies the applicability of the retrieval method. The RMSEs of the downwelling/upwelling shortwave radiation flux and downwelling/upwelling longwave radiation flux were 138.87/32.78 W m−2 and 51.55/17.92 W m−2, respectively, and the RMSEs of net radiation flux, sensible heat flux, and latent heat flux were 58.88 W m−2, 82.56 W m−2 and 72.46 W m−2, respectively. The spatial distributions and diurnal variations of LST and turbulent heat fluxes were further analyzed in detail.

Advances in the Estimation of Global Surface Net Heat Flux Based on Satellite Observation: J-OFURO3 V1.1

The reliability of surface net heat flux data obtained from the latest satellite-based estimation [the third-generation Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO3, V1.1)] was investigated. Three metrics were utilized: (1) the global long-term (30 years) mean for 1988–2017, (2) the local accuracy evaluation based on comparison with observations recorded at buoys located at 11 global oceanic points with varying climatological characteristics, and (3) the physical consistency with the freshwater balance related to the global water cycle. The globally averaged value of the surface net heat flux of J-OFURO3 was −22.2 W m−2, which is largely imbalanced to heat the ocean surface. This imbalance was due to the turbulent heat flux being smaller than the net downward surface radiation. On the other hand, compared with the local buoy observations, the average difference was −5.8 W m−2, indicating good agreement. These results indicate a paradox of the global surface net heat flux. In relation to the global water cycle, the balance between surface latent heat flux (ocean evaporation) and precipitation was estimated to be almost 0 when river runoff from the land was taken into consideration. The reliability of the estimation of the latent heat flux was reconciled by two different methods. Systematic ocean-heating biases by surface sensible heat flux (SHF) and long wave radiation were identified. The bias in the SHF was globally persistent and especially large in the mid- and high latitudes. The correction of the bias has an impact on improving the global mean net heat flux by +5.5 W m−2. Furthermore, since J-OFURO3 SHF has low data coverage in high-latitudes areas containing sea ice, its impact on global net heat flux was assessed using the latest atmospheric reanalysis product. When including the sea ice region, the globally averaged value of SHF was approximately 1.4 times larger. In addition to the bias correction mentioned above, when assuming that the global ocean average of J3 SHF is 1.4 times larger, the net heat flux value changes to the improved value (−11.3 W m−2), which is approximately half the original value (−22.2 W m−2).

Predicting evapotranspiration from drone-based thermography – a method comparison in a tropical oil palm plantation

Abstract. For the assessment of evapotranspiration, near-surface airborne thermography offers new opportunities for studies with high numbers of spatial replicates and in a fine spatial resolution. We tested drone-based thermography and the subsequent application of the DATTUTDUT energy balance model using the widely accepted eddy covariance technique as a reference method. The study site was a mature oil palm plantation in lowland Sumatra, Indonesia. For the 61 flight missions, latent heat flux estimates of the DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature) model with measured net radiation agreed well with eddy covariance measurements (r2 = 0.85; MAE = 47; RMSE = 60) across variable weather conditions and times of day. Confidence intervals for slope and intercept of a model II Deming regression suggest no difference between drone-based and eddy covariance methods, thus indicating interchangeability. The DATTUTDUT model is sensitive to the configuration of the net radiation assessment. Overall, we conclude that drone-based thermography with energy balance modeling is a reliable method complementing available methods for evapotranspiration studies. It offers promising, additional opportunities for fine grain and spatially explicit studies.

Evaluation of alternative two-source remote sensing models in partitioning of land evapotranspiration

Remote sensing (RS)-based two-source models have been widely used to separate soil evaporation (E) and vegetation transpiration (T) in ecosystems. Alternative model assumptions and physical mechanisms employed in different two-source models cause the difference in model performance. Understanding of the characteristics and limitations of alternative two-source models is critical to ensure the most effective applications in various climatic zones and underlying surfaces. This study investigated two types of land surface temperature (LST) decomposition based two-source models, namely Two-Source Energy Balance Model (TSEB) and Modified Pixel Component Arranging and Comparing Algorithm (M-PCACA); as well as a meteorological factors-based Penman-Monteith type model (PM-MU) in the partitioning of E and T. The three models were compared at 21 flux stations across China, which included seven types of underlying surfaces and one isotope station. The study demonstrated that all three models were applicable in a large scale study for latent heat flux (LE) estimation. Overall, M-PCACA performed the best with average root-mean-square errors (RMSE) of 38.7 W/m2, while PM-MU performs the worst with average RMSE of 61.4 W/m2. For ET partitioning, the two models based on LST decomposition (TSEB and M-PCACA) gave more reliable estimates compared with stable water isotope observations. In addition, verification results for the seven types of underlying surfaces showed that the TSEB gave the best accuracy in the areas with high leaf area index (LAI) and vegetation height (e.g. forest), while the PM-MU had an advantage in the areas without vegetation (e.g. deserts and water bodies). Due to the characteristics of the land surface temperature-vegetation coverage (LST-fc) trapezoidal framework, the M-PCACA model had the most stable model performance for various underlying surfaces and gave the best estimates in grassland, wetland, and farmland areas. Using a thorough discussion, this study identified the sources of discrepancies induced by different physical mechanisms of three remote sensing-based two-source models. This investigation provided insights to better understand the alternative two-source models and provides guidance for model application.

On the Along‐Slope Heat Loss of the Boundary Current in the Eastern Arctic Ocean

AbstractThis study presents recent observations to quantify oceanic heat fluxes along the continental slope of the Eurasian part of the Arctic Ocean, in order to understand the dominant processes leading to the observed along‐track heat loss of the Arctic Boundary Current (ABC). We investigate the fate of warm Atlantic Water (AW) along the Arctic Ocean continental margin of the Siberian Seas based on 11 cross‐slope conductivity, temperature, depth transects and direct heat flux estimates from microstructure profiles obtained in summer 2018. The ABC loses on average (108) J m−2 per 100 km during its propagation along the Siberian shelves, corresponding to an average heat flux of 47 W m−2 out of the AW layer. The measured vertical heat flux on the upper AW interface of on average 10 W m−2 in the deep basin, and 3.7 W m−2 above the continental slope is larger than previously reported values. Still, these heat fluxes explain less than 20% of the observed heat loss within the boundary current. Heat fluxes are significantly increased in the turbulent near‐bottom layer, where AW intersects the continental slope, and at the lee side of a topographic irregularity. This indicates that mixing with ambient colder water along the continental margins is an important contribution to AW heat loss. Furthermore, the cold halocline layer receives approximately the same amount of heat due to upward mixing from the AW, compared to heat input from the summer‐warmed surface layer above. This underlines the importance of both surface warming and increased vertical mixing in a future ice‐free Arctic Ocean in summer.

Model-based real-time surface heat flux and temperature estimation for the DIII-D tokamak

A control-oriented model for monitoring of wall power flux densities on the DIII-D tokamak has been successfully implemented and validated experimentally. Future reactors will have to withstand severe steady state high heat flux loads on plasma-facing components (PFCs). Due to the difficulty of directly-measuring local heat fluxes on these components, monitoring and protection of PFCs during the plasma discharge can benefit from simplified physics-based real-time functional models to estimate and guide heat load control. As a first step into the development, a control-oriented model for monitoring of wall power flux densities and temperatures on DIII-D tokamak has been successfully implemented. The paper discusses the experimental demonstration and comparison of the 2D model-based wall heat flux algorithm on the DIII-D inner wall limiter (IWL) against infra-red (IR) camera heat flux measurements for limited plasma configurations. The paper also reports on the benchmarking of the field line tracing environment, SMITER, developed at ITER organization on DIII-D tokamak against experimental IR diagnostic data and the derivation of the component shaping weighting factors for the 2D model-based approach. Extension of the model-based approach for surface temperature estimation on the DIII-D IWL is also presented.

Estimation of Sensible and Latent Heat Fluxes Using Surface Renewal Method: Case Study of a Tea Plantation

An experiment of sensible and latent heat flux measurement was conducted in a tea plantation near the Yangtze River within Danyang of Jiangsu Province, China. High-frequency (~10 Hz) air temperature measurement with fine-wire thermocouples (⌀ = 50 μm) was used for the estimation of sensible heat flux (H), and latent heat flux (LE) was extracted as a residual of the energy balance equation using additional measurements of net radiation (Rn) and soil heat flux (G). Results were compared against the eddy covariance (EC) system under unstable conditions only, and days with high precipitation were excluded from further analysis. Half-hourly datasets of the sensible heat flux estimated using the surface renewal method (SR) (HSR) and measured by the EC system (HEC) were analyzed. Results showed good agreement with R2 = 0.80, root mean square error (RMSE) = 27.87 W m−2, relative error (RE) = 9.02%, and a regression slope of 0.68—this slope was used for the calibration of the uncalibrated HSR estimated by SR. On the other hand, the half-hourly dataset of LESR was regressed against EC, and it showed good agreement with relatively high R2 = 0.93, RMSE = 32.99 W·m−2, and RE = 5.67%. Hence, the SR method may estimate the surface fluxes at a relatively low cost, ultimately improving calculations of evapotranspiration. Thus, the SR method could provide an economical tool for improving crop water management of tea plantations.

Inverse Estimation of Time-Dependent Heat Flux in Stagnation Region of Annular Jet on a Cylinder Using Levenberg–Marquardt Method

All solving methods available in literature are formulated for direct solution of stagnation point flow and its heat transfer impinging on the surfaces with known boundary conditions. In this paper for the first time, a numerical code based on Levenberg–Marquardt method is presented for solving the inverse heat transfer problem of an annular jet on a cylinder and estimating the time-dependent heat flux using temperature distribution at a specific point. For this purpose, the numerical solution of the dimensionless temperature and the convective heat transfer in a radial incompressible flow on a cylinder rod is carried out as a direct problem. Using similarity variable and appropriate transformations, momentum and energy equations are converted into Semi-similar equations. The heat flux is then estimated by applying the Levenberg–Marquardt approach. This technique is an iterative approach based on minimizing the least-square summation of the error values, the error being the difference between the estimated and measured temperatures. Results of the inverse analysis indicate that the Levenberg–Marquardt algorithm is an efficient and acceptably stable technique for estimating heat flux. This method also exhibits considerable stability for noisy input data. The maximum value of the sensitivity coefficient is related to the estimation of exponential heat flux and its value is 0.1619 also the minimum value of the sensitivity coefficient is 5.6210-6 which is related to the triangular heat flux. The results show that the parameter estimation error in calculating the triangular and trapezoidal heat flux is greater than the exponential and sinus–cosines heat flux because the maximum value of RMS error is obtained for these two cases, which are 0.481 and 0.489, respectively the reason for the increase in the errors in estimating these functions is the existence of points where the first derivative of the function does not exist.

LBM combined with LM algorithm to estimate the unknown heat flux – A new inverse approach

The objective of the present work is the application of the Levenberg–Marquardt method as an inverse method for the estimation of the heat flux. In this paper inverse estimation of heat flux for a two-dimensional heat conduction problem is carried out. As a direct method, in the first attempt the solution of two-dimensional inverse heat conduction problem is formulated by using Lattice Boltzmann Method as a forward model. Later the solution to the problem is also obtained by using Finite Difference Method (FDM) as the forward model for the purpose of validation. Once the forward model is established, Levenberg-Marquardt Method is used as an inverse model to estimate the input parameter i.e. heat flux which is reported. A complete error analysis of inverse model with known values is performed. As the Lattice Boltzmann Method (LBM) is acclimatizing to parallel computation, its use is recommended in Levenberg-Marquardt method for the solution of inverse heat conduction problem which is evident from the results.

Pixel-Based Calibration and Atmospheric Correction of a UAS-Mounted Thermal Camera for Land Surface Temperature Measurements

HighlightsA novel pixel-based calibration algorithm and an atmospheric correction method are developed.Application of the calibration methods reduces the RMSE of measurements to less than 1.32°C.The calibrations facilitate stitching of images together to form whole-field mosaics.Abstract. Thermal imagery can be used to provide insight into the water stress status and evapotranspiration demand of crops, but satellite-based sensors are generally too coarse spatially and too infrequent temporally to provide information of use for the management of specific fields. Thermal cameras mounted on small unmanned aerial systems (UAS) have potential to provide canopy temperature information at high spatial and temporal resolutions useful for crop management; however, without appropriate camera corrections, the measurement biases of these uncooled thermal cameras can be larger than ±5°C. Such uncertainty can render such camera measurements useless. In this research, a pixel-based (non-uniformity) calibration algorithm and an atmospheric correction method based on in-field approximate blackbody sources (water targets) were developed for a thermal camera. The objective was to improve the temperature measurement accuracy of the thermal camera on various land surfaces including soil and vegetation. With sufficient accuracy, temperature measurements can be used for the estimation of latent heat flux of field crops in the future. The thermal camera was first calibrated in a laboratory setting where the camera and environmental conditions were controlled. The results indicated that in the range between 10°C and 45°C, the calibrated temperatures were accurate, with an average bias of 1.76°C, and had a high linear correlation with reference temperatures (water target temperatures) (R2 > 0.99). Variability of measurements was also better constrained. In-field atmospheric correction is also important for obtaining high-accuracy thermal imagery. By applying both pixel-based calibration and atmospheric corrections, the RMSE (root mean square error) of validation targets from two dates in 2017 was reduced from 4.56°C and 6.36°C before calibration to 1.32°C and 1.24°C after calibration. The calibration process also increased the range of temperatures in the imagery, which enhanced contrast and may help with identification of tie-points and stitching of images together to form whole-field mosaics. Keywords: Atmospheric correction, Pixel-based calibration, Thermal remote sensing, UAS, Water targets.

Comparison of the differences of sensible heat flux observation methods at different spatial scales in North China Plain

Understanding the estimation of sensible heat flux on heterogeneous underlying surface of farmland is of great environmental importance for alleviating ecological pollution and adapting agricultural water management. Data measurements were conducted to evaluate the difference of sensible heat flux under large aperture scintillometer (H-las) and eddy covariance (H-ec) under various underlying surfaces in North China Plain. Four typical periods represented as the crop periods (January, March, May, and August) were selected to investigate the correlation between the H-las and H-ec as well as relative error assessment. Results showed that the H-las and H-ec were consistent on the non-uniform underlying surface as time; the correlation coefficient (R2) were 0.75 in January, 0.65 in March, 0.65 in May, and 0.60 in August, respectively; the coverage of LAS footprints was 45 times that of single-point observations. In the prevailing wind direction, most of the EC source areas were homogeneous farmland, while the LAS source areas also include residential areas and woodland; the H-las was slightly higher than H-ec by 1.61–21.21%, which results from the non-uniformity of underlying surface, the variation of meteorological factors, the inconsistency source areas, and the deviation of EC. The method of LAS is recommended for measuring average sensible heat flux in this study area accordingly.

The Importance of Including Sea Surface Current when Estimating Air–Sea Turbulent Heat Fluxes and Wind Stress in the Gulf Stream Region

AbstractUsing buoy observations from 2004 to 2010 and newly released atmospheric reanalysis and satellite altimetry-derived geostrophic currents from 1993 to 2017, the quantitative contribution of daily mean surface currents to air–sea turbulent heat flux and wind stress uncertainties in the Gulf Stream (GS) region is investigated based on bulk formulas. At four buoy stations, the daily mean latent (sensible) heat flux difference between the estimates with and without surface currents range from −18 (−4) to 20 (4) W m−2, while the daily mean wind stress difference ranges from −0.04 to 0.02 N m−2. The positive values indicate higher estimates with opposite directions between surface currents and absolute winds. The transition between positive and negative differences is significantly associated with synoptic-scale weather variations. The uncertainties based on buoy observations are approximately 7% and 3% for wind stress and turbulent heat fluxes, respectively. The new reanalysis and satellite geostrophic currents confirm the uncertainties identified by buoy observations with acceptable discrepancies and provide a spatial view of the uncertainty fields. The mean geostrophic currents are aligned with the surface wind along the GS; therefore, the turbulent heat fluxes and wind stress will be “underestimated” with surface currents included. However, on both sides of the GS, the surface flow can be upwind due to possible mechanisms of eddy–mean flow interactions and recirculations, resulting in higher turbulent heat flux estimations. The wind stress and turbulent heat flux uncertainties experience significant seasonal variations and show long-term trends.

Identification of Dominant Warm-Season Latent Heat Flux Patterns in the Lower Mississippi River Alluvial Valley

Warm-season precipitation in the Lower Mississippi River Alluvial Valley (LMRAV) is heavily dominated by the rates of evapotranspiration and surface heat fluxes and is a primary water resource for agriculture. However, the stochastic nature of LMRAV warm-season thunderstorms makes precipitation forecasts challenging. The Weather Research and Forecasting Hydrologic (WRF-Hydro) model, coupled with the multi-parameter Noah land surface (Noah-MP) model, has improved estimates of important warm-season precipitation process. Given the widespread agriculture and dominance of crop and forested landscapes over the region, proper assessment of land use / land cover (LULC) is critical in predicting warm-season precipitation patterns. The objective of this study is to quantify simulated latent heat flux sensitivity (important for warm-season precipitation) to temporally updated LULC datasets. Both the model default and annually updated LULC conditions were used to initialize a 16-year WRF-Hydro simulation from which warm-season latent heat flux estimates were obtained. Annual root mean square difference was computed at each gridpoint. Cluster analysis preprocessed with kernel principal component analysis was used to identify spatial RMSD structures that quantified sensitivity to updated LULC conditions. Results showed the largest impacts occurred directly in the LMRAV and for points slightly east and revealed a meteorological link between these regions.

Ignition/non-ignition phase transition: A new critical heat flux estimation method

Flaming ignition properties of dead and live thin wildland porous fuels submitted to different incident heat flux intensities are examined experimentally using a cone calorimeter. Data are compared to analytical and numerical results of a model that uses an energy balance and includes energy and temperature ignition criteria. The model provides the linear trend of ignition time for large heat flux intensities, but fails to reproduce the behavior of data near threshold ignition, because it does not include explicitly volatiles emission at pyrolysis. A new method is proposed for the estimation of the critical heat flux for ignition of porous fuels, based on the ignition time behavior as a power-law that characterizes phase transitions theory. The critical heat flux for ignition thus estimated has been found small compared to literature data. The discrepancy is due to the probabilistic ignition behavior observed in the critical region and ignored by literature that uses deterministic methods for the estimation of the critical heat flux for ignition. The heterogeneities of fuel particles composition and arrangement could be the major causes of such a probabilistic aspect.