Using the Sensible Heat Flux Eddy Covariance-Based Exchange Coefficient to Calculate Latent Heat Flux from Moisture Mean Gradients Over Snow.

In absence of the high-frequency measurements of wind components, sonic temperature and water vapour required by the eddy covariance (EC) method, Monin-Obukhov similarity theory (MOST) is often used to calculate heat fluxes. However, MOST requires assumptions of stability corrections and roughness lengths. In most environments and weather situations, roughness length and stability corrections have high uncertainty. Here, we revisit the modified Bowen-ratio method, which we call C-method, to calculate the latent heat flux over snow. In the absence of high-frequency water vapour measurements, we use sonic anemometer data, which have become much more standard. This method uses the exchange coefficient for sensible heat flux to estimate latent-heat flux. Theory predicts the two exchange coefficients to be equal and the method avoids assuming roughness lengths and stability corrections. We apply this method to two datasets from high mountain (Alps) and polar (Antarctica) environments and compare it with MOST and the three-layer model (3LM). We show that roughness length has a great impact on heat fluxes calculated using MOST and that different calculation methods over snow lead to very different results. Instead, the 3LM leads to good results, in part due to the fact that it avoids roughness length assumptions to calculate heat fluxes. The C-method presented performs overall better or comparable to established MOST with different stability corrections and provides results comparable to the direct EC method. An application of this method is provided for a new station installed in the Pamir mountains. Supplementary Information: The online version contains supplementary material available at 10.1007/s10546-024-00864-y.

Applicability of the generalized wind profile model over mountainous forests.

The analytical equation based on Monin-Obukhov (M-O) similarity theory (i.e., wind profile equation) has been adopted since 1970s for using in the prediction of wind vertical profile over flat terrains, which is mature and accurate. However, its applicability over complex terrains remains unknown. This applicability signifies the accuracy of the estimations of aerodynamic parameters for the boundary layer of non-flat terrain, such as zero-displacement height (d) and aerodynamic roughness length (z0), which will determine the accuracy of frequency correction and source area analysis in calculating carbon, water, and trace gas fluxes based on vorticity covariance method. Therefore, the validation of wind profile model in non-flat terrain is the first step to test whether the flux model needs improvement. We measured three-dimensional wind speed data by using the Ker Towers (three towers in a watershed) at Qingyuan Forest CERN in the Mountainous Region of east Liaoning Province, and compared them with data from Panjin Agricultural Station in the Liaohe Plain, to evaluate the applicability of a generalized wind profile model based on the Monin-Obukhov similarity theory on non-flat terrain. The results showed that the generalized wind profile model could not predict wind speeds accurately of three flux towers separately located in different sites, indicating that wind profile model was not suitable for predicting wind speeds in complex terrains. In the leaf-off and leaf-on periods, the coefficient of determination (R2) between observed and predicted wind speeds ranged from 0.12 to 0.30. Compared to measured values, the standard error of the predicted wind speeds was high up to 2 m·s-1. The predicted wind speeds were high as twice as field-measured wind speed, indicating substantial overestimation. Nevertheless, this model correctly predicted wind speeds in flat agricultural landscape in Panjin Agricultural Station. The R2 between observed wind speeds and predicted wind speed ranged from 0.90 to 0.93. The standard error between observed and predicted values was only 0.5 m·s-1. Results of the F-test showed that the root-mean-square error of the observed and predicted wind speeds in each secondary forest complex terrain was much greater than that in flat agricultural landscape. Terrain was the primary factor affecting the applicability of wind profile model, followed by seasonality (leaf or leafless canopy). The wind profile model was not applicable to the boundary-layer flows over forest canopies in complex terrains, because the d was underestimated or both the d and z0 were underestimated, resulting in inaccurate estimation of aerodynamic height.

Research on a Multimodel Fusion Diagnosis Method for Evaporation Ducts in the East China Sea.

Evaporation ducts are abnormal states of the atmosphere in the air-sea boundary layer that directly affect the propagation trajectory of electromagnetic (EM) waves. Therefore, an accurate diagnosis of the evaporation duct height (EDH) is important for studying the propagation trajectory of EM waves in evaporation ducts. Most evaporation duct models (EDMs) based on the Monin-Obukhov similarity theory are empirical methods. Different EDMs have different levels of environmental adaptability. Evaporation duct diagnosis methods based on machine learning methods only consider the mathematical relationship between data and do not explore the physical mechanism of evaporation ducts. To solve the above problems, this study observed the meteorological and hydrological parameters of the five layers of the low-altitude atmosphere in the East China Sea on board the research vessel Xiangyanghong 18 in April 2021 and obtained the atmospheric refractivity profile. An evaporation duct multimodel fusion diagnosis method (MMF) based on a library for support vector machines (LIBSVM) is proposed. First, based on the observed meteorological and hydrological data, the differences between the EDH diagnosis results of different EDMs and MMF were analyzed. When ASTD ≥ 0, the average errors of the diagnostic results of BYC, NPS, NWA, NRL, LKB, and MMF are 2.57 m, 2.92 m, 2.67 m, 3.27 m, 2.57 m, and 0.24 m, respectively. When ASTD < 0, the average errors are 2.95 m, 2.94 m, 2.98 m, 2.99 m, 2.97 m, and 0.41 m, respectively. Then, the EM wave path loss accuracy analysis was performed on the EDH diagnosis results of the NPS model and the MMF. When ASTD ≥ 0, the average path loss errors of the NPS model and MMF are 5.44 dB and 2.74 dB, respectively. When ASTD < 0, the average errors are 5.21 dB and 3.46 dB, respectively. The results show that the MMF is suitable for EDH diagnosis, and the diagnosis accuracy is higher than other models.

Numerical simulation of airflow and dispersion in 3D street canyons: the effect of atmospheric temperature stratification.

Based on the Reynolds-averaged Navier-Stokes (RANS) method, a modified standard k-ϵ turbulence model with a source term rooted on Monin-Obukhov similarity theory was used to investigate the effects of temperature stratifications on the flow field and pollutant dispersion in 3D street canyons. The results showed that airflow and pollutant transport was highly dependent on temperature stratifications. The vortex velocity in the canyon varied with temperature stratifications. With the more unstable conditions, the centre of the vortex was closer to the ground, and the intensity and turbulent kinetic energy of the eddy increased. Because the source of pollution was located on the ground, the pollutants migrated to the leeward side of the building with the eddy and then moved upstream or downstream with the airflow. Under neutral conditions, the pollutants migrated to the leeward side first with the eddy in the canyon and then mainly migrated downstream with the airflow but rarely migrated to the upstream street canyons. However, under unstable conditions, the pollutant concentration in the upstream street canyons also increased, mainly because the intensity of the vortex increased, which made the pollutant easier to transport upstream. According to the results of the pollutant flux analysis, the turbulent fluxes on the top plane of the buildings were positive and increased significantly with the more unstable conditions, indicating that turbulent fluxes play a positive leading role in the dispersion of pollutants. The transverse transport of the pollutants was mainly dominated by convective motion, and turbulent transport was relatively minor.

Evidence of Strong Flux Underestimation by Bulk Parametrizations During Drifting and Blowing Snow.

The influence of drifting and blowing snow on surface mass and energy exchange is difficult to quantify due to limitations in both measurements and models, but is still potentially very important over large areas with seasonal or perennial snow cover. We present a unique set of measurements that make possible the calculation of turbulent moisture, heat, and momentum fluxes during conditions of drifting and blowing snow. From the data, Monin-Obukhov estimation of bulk fluxes is compared to eddy-covariance-derived fluxes. In addition, large-eddy simulations with sublimating particles are used to more completely understand the vertical profiles of the fluxes. For a storm period at the Syowa S17 station in East Antarctica, the bulk parametrization severely underestimates near-surface heat and moisture fluxes. The large-eddy simulations agree with the eddy-covariance fluxes when the measurements are minimally disturbed by the snow particles. We conclude that overall exchange over snow surfaces is much more intense than current models suggest, which has implications for the total mass balance of the Antarctic ice sheet and the cryosphere. SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s10546-021-00653-x.