Surface Layer Parameterization Research Articles

Study of surface layer characteristics in the presence of suspended snow particles using observational data and Large-Eddy Simulation

The snowdrift is a two-phase flow consisting of air and suspended particles. In the presence of snow particles in the air, additional stability appears in the surface layer due to the density gradient. The density gradient reduces turbulence and affects the properties of the surface layer. Therefore, to describe the properties of the flow with included snow particles, additional clarifications are required. A description of the surface layer parameterization with the presence of suspended snow particles is presented in this paper. The formulation of the effect of snow particles consists in reformulation of the Obukhov turbulent length scale. The novel surface layer parameterization allows to take into account the effect of snow particles on turbulent flow and may improve the estimates of friction velocity and boundary-layer height.The parameterization was successfully tested on the observational data. Description of snow particles influence was included in the Large-Eddy Simulation (LES) model. The numerical experiments confirmed an increase in the stability of the surface layer. Mechanism of suspended particles influence on the surface layer is analogous to a thermal stabilization of the turbulent flow, in which negative buoyancy acts to reduce the turbulent kinetic energy.

Enhancing Surface Wind Speed and Temperature Prediction Using Surface-Layer Emulator and Transfer Learning

Abstract Accurate prediction of surface wind speed and temperature is crucial for many sectors. Physical schemes in numerical weather prediction (NWP) and data-driven correction approaches have limitations due to uncertainties in parameterization and lack of robustness, respectively. This study introduces a physics-informed data model, PhyCorNet, which combines a deep learning–based physics emulator (PhyNet) and a subsequent correction network (CorNet). PhyNet imitates the revised surface-layer parameterization scheme [default option of the Weather Research and Forecasting (WRF) Model]. CorNet refines predictions by mitigating the difference between PhyNet and observations. PhyCorNet enables gridded forecasts despite the training data being point based. Therefore, PhyCorNet can be regarded as a rediagnostic scheme for surface wind speed at 10 m (WS10) and temperature at 2 m (T2). Compared to WRF, it reduced diagnostic root-mean-square errors in the 24-h forecasts for WS10 and T2 by 40% and 36%, respectively, across China, with unbiased forecasts at almost all sites. PhyCorNet addresses the oversmoothing prediction issue of other deep learning models by providing the ability to represent fine-scale features and perform well in statistically extreme samples. In grid cells without observations for training, PhyCorNet performed much better than WRF, demonstrating the zero-shot learning capability. This study implies that the emulator plus bias correction provided by PhyCorNet could be used as a simple but effective approach to improve the performance of other diagnostic quantities in NWP. Significance Statement We developed a new model called PhyCorNet to improve the surface wind speed and temperature forecasts. Existing weather forecast models have limitations in accurately predicting these variables. PhyCorNet first uses a neural network (PhyNet) to mimic an existing physics-based wind and temperature calculation method. Another neural network [correction network (CorNet)] improves the PhyNet forecasts by comparing them with observations. Across China, PhyCorNet reduced 24-h forecast errors for wind speed by 40% and temperature by 36% compared to a conventional model. It performed well under diverse conditions and overcame the oversmoothing issues faced by other machine learning models. Importantly, PhyCorNet outperformed traditional models in areas without historical observations. We suggest that this new framework can also be used to enhance the forecasts of other atmospheric variables.

Comprehensive Comparison of Seven Widely-Used Planetary Boundary Layer Parameterizations in Typhoon Mangkhut Intensification Simulation

Numerical experiments using the WRF model were conducted to analyze the sensitivity of Typhoon Mangkhut intensification simulations to seven widely used planetary boundary layer (PBL) parameterization schemes, including YSU, MYJ, QNSE, MYNN2, MYNN3, ACM2, and BouLac. The results showed that all simulations generally reproduced the tropical cyclone (TC) track and intensity, with YSU, QNSE, and BouLac schemes better capturing intensification processes and closely matching observed TC intensity. In terms of surface layer parameterization, the QNSE scheme produced the highest Ck/Cd ratio, resulting in stronger TC intensity based on Emanuel’s potential intensity theory. In terms of PBL parameterization, the YSU and BouLac schemes, with the same revised MM5 surface layer scheme, simulated weaker turbulent diffusivity Km and shallower mixing height, leading to stronger TC intensity. During the intensification period, the BouLac, YSU, and QNSE PBL schemes exhibited stronger tangential wind, radial inflow within the boundary layer, and updraft around the eye wall, consistent with TC intensity results. Both PBL and surface layer parameterization significantly influenced simulated TC intensity. The QNSE scheme, with the largest Ck/Cd ratio, and the YSU and BouLac schemes, with weaker turbulent diffusivity, generated stronger radial inflow, updraft, and warm core structures, contributing to higher storm intensity.

Sensitivity of Coastal Squall-Line Evolution to Numerical Sea-Breeze Initialization Method

Abstract This study employs 3D idealized numerical experiments to investigate the physical processes associated with coastal convection initiation (CI) as an offshore-moving squall line traverses a mountainous coastal region. A squall line can propagate discretely as convection initiates over the lee slope downstream of the primary storm as the cold pool collides with a sea breeze. Intensity of the initiating convection, thus the downstream squall line, is sensitive to the sea-breeze numerical initialization method, since it influences sea-breeze and cold pool characteristics, instability and vertical wind shear in the sea-breeze environment, and ultimately the vertical acceleration of air parcels during CI. Here, sea breezes are generated through four commonly used numerical methods: a cold-block marine atmospheric boundary layer (MABL), a prescribed surface sensible heat flux function, a prescribed surface sensible plus latent heat flux functions, and radiation plus surface-layer parameterization schemes. For MABL-initialized sea breezes, shallow weak sea-breeze flow in a relatively low instability environment results in weak CI. For the remainder, deeper stronger sea-breeze flow in an environment of enhanced instability supports more robust CI. In a subset of experiments, however, the vertical trajectory of air parcels is suppressed leading to weaker convection. Downward acceleration forms due to the horizontal rotation of the sea-breeze flow. Accurate simulations of coastal convective storms rely on an accurate representation of sea breezes. For idealized experiments such as the present simulations, a combination of initialization methods likely produces a more realistic representation of sea breeze and the associated physical processes.

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Abstract Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

Оценка точности воспроизведения метеорологических характеристик по модели WRF для акватории Каспийского моря

The paper presents the accuracy estimates for the simulation of meteorological characteristics obtained from the WRF model with a spatial resolution of 6 km adapted for the Caspian Sea. It was used to simulate meteorological characteristics using various parameterizations of physical processes available in the WRF model. The model was verified using data from coastal weather stations for 2021. It was found that the best results in reproducing meteorological characteristics are achieved using the Thompson graupel scheme as a parameterization of physical processes, the Tiedtke scheme as a parameterization of convection, the RRTMG scheme as a parameterization of long-wave and short-wave radiation, the MYJ scheme as a boundary layer parameterization, and the Noah Land-Surface Model as a surface layer parameterization. An analysis of the simulation results versus observational data from weather stations showed that the model correctly reproduces the modulus and direction of wind speed, air temperature, and sea-level pressure. It was found that the model correctly reproduces not only the average characteristics of wind speed, but also its extreme values, which is especially important when modeling storm surges in the Caspian Sea. Keywords: Caspian Sea, regional nonhydrostatic atmosphere circulation model, numerical methods

An update to WRF surface layer parameterization over an Indian region

Surface layer parameterization schemes in numerical weather prediction models are based on the Monin-Obukhov similarity theory (MOST), which utilizes empirical functions to incorporate the effects of near-surface atmospheric stability. In the present study, an effort has been made to implement and evaluate the performance of recently developed similarity functions under stable stratification in the surface layer parameterization of Weather Research and Forecasting Model version 4.2.2 (WRFv4.2.2). For this purpose, the commonly used revised version of MM5 surface layer module in WRF model is updated using the similarity functions suggested by Srivastava et al. (2020). The model is configured with three nested domains around the flux tower installed at Ranchi (23.412° N, 85.440°E), India. The simulations are carried out for a complete year, and the model simulated near-surface atmospheric variables are compared with the observations. The study reveals that updated similarity functions lead to a noticeable improvement in WRF model performance. In particular, the modified scheme reduced the mean absolute error and root mean square error for 10-m wind speed (2-m temperature) by about 22 % (10 %) and 23 % (8 %), respectively, with improved correlation coefficients during January. The analysis suggests that the new similarity functions could potentially be used in weather forecast model over the Indian region.

On the Efficacy of Monin–Obukhov and Bulk Richardson Surface-Layer Parameterizations over Drylands

Abstract Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical component of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin–Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have recently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations’ applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January–31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity (), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations’ performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., ) and turbulence (i.e., TKE) quantities, although caution is needed when applying the Rib H parameterizations to drylands. These findings represent an important milestone for the applicability of Rib parameterizations, given the large fraction of Earth’s surface covered by drylands. Significance Statement Weather forecasting models rely upon complex mathematical relationships to predict temperature, wind, and moisture. Monin–Obukhov similarity theory (MOST) has long been used to forecast these quantities near the land surface, even though MOST’s limitations are well known in the scientific community. Researchers have suggested an alternative to MOST called the bulk Richardson (Rib) approach. To allow for the Rib approach to be used in weather forecasting models, the approach needs to be tested over different land-cover and climate types. In this study, we applied the Rib approach to dry areas of the United States and found that the approach better represented turbulence variables than MOST relationships. These findings are an important step toward using Rib relationships in weather forecasting models.

Evaluating Atmospheric Surface Layer Flux Parameterization within the Coastal Regime

Abstract Traditional atmospheric surface layer theory assumes homogeneous surface conditions. Regardless, nearly all surface layer parameterization schemes employed within numerical weather prediction models utilize the same techniques within highly heterogeneous coastal regimes as for homogeneous environments. We compare predicted surface weather and fluxes of momentum, heat, and moisture—focusing mainly on momentum—from regional simulations using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model to observations collected from offshore buoys, inland flux towers, and radiosonde profiles during the Coastal Land-Air-Sea Interaction (CLASI) project throughout the summer of 2021 around Monterey Bay, California. Results reveal that modeled cross-coastal surface flux gradients are spuriously discontinuous, leading to systematically overestimated fluxes and weak winds inland of the coastline during onshore flow periods. Additionally, contrary to observations, modeled surface exchange coefficients are insensitive to wind direction on both sides of the coast, which degrades predictive skill downstream from the coastline. Over the central bay, prediction degrades when near-surface wind directions deviate from the prevailing flow direction as the parameterized stress–wind relationship fails during these cases. Predictive skill over the bay is therefore linked to variations in wind direction. Offshore of the geographically complex peninsula, systematic biases are less clear; however, bifurcations in drag coefficients based on wind direction were measured here as well. Last, increasing the horizontal grid spacing from 333 m to 3 km does not significantly affect surface layer prediction. This work highlights the need to reevaluate surface layer parameterization methods for modeling within coastal regions. Significance Statement Understanding surface layer weather is critical for many purposes, such as infrastructure design and weather forecasting. Within the context of numerical modeling and weather prediction, skillful forecasts of surface winds and temperature rely on accurate portrayal of the surface layer. By comparing observations collected during the Coastal Land-Air-Sea Interaction field program to numerical model solutions, we show that prediction of the surface layer fluxes of momentum, heat, and moisture break down near the coastline, which leads to biases in the predicted surface layer weather both inland and over the water. As surface layer parameterization methods across nearly all numerical models are rooted in the same practices, our results call into question the use of traditional methods near the coastline.

Evaluation and Improvement of a TKE-Based Eddy-Diffusivity Mass-Flux (EDMF) Planetary Boundary Layer Scheme in Hurricane Conditions

Abstract Accurately representing boundary layer turbulent processes in numerical models is critical to improve tropical cyclone forecasts. A new turbulence kinetic energy (TKE)-based moist eddy-diffusivity mass-flux (EDMF-TKE) planetary boundary layer scheme has been implemented in NOAA’s Hurricane Analysis and Forecast System (HAFS). This study evaluates EDMF-TKE in hurricane conditions based on a recently developed framework using large-eddy simulation (LES). Single-column modeling tests indicate that EDMF-TKE produces much greater TKE values below 500-m height than LES benchmark runs in different high-wind conditions. To improve these results, two parameters in the TKE scheme were modified to ensure a match between the PBL and surface-layer parameterizations. Additional improvements were made by reducing the maximum allowable mixing length to 40 m based on LES and observations, by adopting a different definition of boundary layer height, and by reducing nonlocal mass fluxes in high-wind conditions. With these modifications, the profiles of TKE, eddy viscosity, and winds compare much better with LES results. Three-dimensional idealized simulations and an ensemble of HAFS forecasts of Hurricane Michael (2018) consistently show that the modified EDMF-TKE tends to produce a stronger vortex with a smaller radius of maximum wind than the original EDMF-TKE, while the radius of gale-force wind is unaffected. The modified EDMF-TKE code produces smaller eddy viscosity within the boundary layer compared to the original code, which contributes to stronger inflow, especially within the annulus of 1–3 times the radius of maximum wind. The modified EDMF-TKE shows promise to improve forecast skill of rapid intensification in sheared environments.

CLASI: Coordinating Innovative Observations and Modeling to Improve Coastal Environmental Prediction Systems

Abstract The Coastal Land–Air–Sea Interaction (CLASI) project aims to develop new “coast-aware” atmospheric boundary and surface layer parameterizations that represent the complex land–sea transition region through innovative observational and numerical modeling studies. The CLASI field effort involves an extensive array of more than 40 land- and ocean-based moorings and towers deployed within varying coastal domains, including sandy, rocky, urban, and mountainous shorelines. Eight Air–Sea Interaction Spar (ASIS) buoys are positioned within the coastal and nearshore zone, the largest and most concentrated deployment of this unique, established measurement platform. Additionally, an array of novel nearshore buoys and a network of land-based surface flux towers are complemented by spatial sampling from aircraft, shore-based radars, drones, and satellites. CLASI also incorporates unique electromagnetic wave (EM) propagation measurements using a coherent array, drone receiver, and a marine radar to understand evaporation duct variability in the coastal zone. The goal of CLASI is to provide a rich dataset for validation of coupled, data assimilating large-eddy simulations (LES) and the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). CLASI observes four distinct coastal regimes within Monterey Bay, California (MB). By coordinating observations with COAMPS and LES simulations, the CLASI efforts will result in enhanced understanding of coastal physical processes and their representation in numerical weather prediction (NWP) models tailored to the coastal transition region. CLASI will also render a rich dataset for model evaluation and testing in support of future improvements to operational forecast models.

Influence of Planetary Boundary Layer (PBL) Parameterizations in the Weather Research and Forecasting (WRF) Model on the Retrieval of Surface Meteorological Variables over the Kenyan Highlands

Regional climate models (RCMs) are crucial for climate studies and may be an alternative source of meteorological data in data-scarce regions. However, the effectiveness of the numerical weather prediction (NWP) models applied in RCMs is hampered by the parameterization of unresolved physical processes in the model. A major source of uncertainties in NWP models is the parameterization of the planetary boundary layer (PBL). This study evaluates the influence of seven PBL parameterization schemes in the Weather Research and Forecasting (WRF) model on the retrieval of four meteorological variables over the Kenyan highlands. The seven PBL schemes consist of four local schemes: the Mellor-Yamada-Janjic (MYJ), Mellor-Yamada-Nakanishi-Niino (MYNN), Bougeault-Lacarrere (BouLac), quasinormal scale elimination (QNSE), and three nonlocal schemes: asymmetrical convective model version 2 (ACM2), Shin and Hong (SHIN) and Yonsei University (YSU). The forcing data for the WRF model was obtained from the fifth generation of the European ReAnalysis (ERA5) dataset. The results were validated against observational data from the Trans-African Hydro-Meteorological Observatory (TAHMO). WRF was found to simulate surface meteorological variables with spatial details coherent with the complex topography within the Kenyan highlands, irrespective of the PBL scheme. A comparison between 2-meter temperature (T2) derived from the YSU scheme and T2 from the land component of ERA5 (ERA5-Land) indicates that surface meteorological variables derived from WRF are better suited for applications over the Kenyan highlands. The choice of the PBL scheme was found to primarily influence the simulation of the 10-meter wind speed (WS10) and rainfall as opposed to T2 and the 2-meter relative humidity (RH2). The insensitivity of the 2-meter variables to the choice of the PBL scheme is attributed to the influence of the surface layer parameterization near the surface. Results from the rainfall simulation indicate that the YSU scheme provides a more realistic depiction of PBL dynamics within the study area. Hence, the YSU scheme is best suited for simulating surface meteorological variables over the Kenyan highlands.

A note on surface layer parameterizations in the weather research and forecast model

Two surface layer parameterization schemes along with five planetary boundary layer (PBL) schemes in the Weather Research and Forecasting model (WRF) are analyzed in order to evaluate the performance of the WRF model in simulating the surface variables and turbulent fluxes over an Indian sub-continent region. These surface layer schemes are based on the fifth-generation Pennsylvania State University—National Center for Atmospheric Research Mesoscale Model (MM5) parameterization; (a) Old MM5 scheme having Businger-Dyer similarity functions and (b) revised MM5 scheme utilizing the functions that are valid for full ranges of atmospheric stabilities. The study suggests that each PBL scheme can reproduce the diurnal variation of 2 m temperature, momentum flux and sensible heat flux irrespective of the surface layer scheme used for the simulations. However, a comparison of model-simulated values of surface variables and turbulent fluxes with observed values suggests that each PBL scheme is found to systematically over-estimate the nocturnal 2 m temperature and 10 m wind speed with both the revised and old schemes during stable conditions.

Surface Layer Turbulent Characteristics over the Complex Terrain of the Loess Plateau Semiarid Region

Surface layer turbulence has an important influence on land-air interactions and pollutant dispersion, and studying the characteristics of surface layer turbulence in complex terrain can contribute to understanding land-air interactions, improving model surface layer parameterization, and enhancing pollution prediction capabilities. The surface layer turbulence observations from the Semi-Arid Climate and Environment Observatory (SACOL) of Lanzhou University in 2008 were processed in this study. High-quality turbulence parameters were calculated, the statistical and transfer characteristics of turbulence were analyzed, and the formation of turbulence was assessed in terms of dynamics and thermodynamics. The atmospheric stability in the semiarid region of the Loess Plateau is basically dominated by neutral/near-neutral and weakly unstable/weakly stable conditions; this pattern is significantly different from the preponderance of unstable and stable layers at other sites. The turbulence intensity differs significantly in both the horizontal and vertical directions and basically shows the relationship I u ≈ I v > I w . The mean values of I u and I v are 0.42 and 0.40, respectively, and the mean value of I w is 0.14, which is different from the general pattern of I u > I v > I w , indicating that the turbulence intensity at SACOL is characterized by a large lateral wind contribution. The dimensionless standard deviations of the nonneutral wind velocity components satisfy the “1/3rd law,” and the dimensionless standard deviations of u, v , and w components are 3.35, 2.98, and 1.26, respectively, in the semiarid Loess Plateau. These values are larger than those over flat terrain. The contribution of the shear term to the formation of turbulence is greater than that of the buoyancy term, and the mean annual values of the shear term and the buoyancy in the kinetic energy budget equation in SACOL are 47.94 × 10−4 m2·s−3 and 11.32 × 10−4 m2·s−3, respectively. The annual mean values of the momentum transfer coefficient C D and the heat transfer coefficient C H under near-neutral conditions are 8.54 × 10−3 and 2.52 × 10−3, respectively.

Numerical Simulation of Near-Surface Atmospheric Conditions during a Radiation Fog over the Complex Terrain

Numerical simulation of near-surface atmosphere during a radiation fog episode over the complex terrain is conducted. Results indicate that, the simulation of near-surface atmospheric wind, temperature, and humidity are very sensitive to land surface and boundary layer parameterization schemes. Simulation impacts of land surface and boundary layer scheme are manifested mainly in near-surface thermodynamic and dynamic quantities, respectively. The NOAH land surface scheme evidently overestimates the near-surface temperature, while underestimates the near-surface dew point and humidity during the fog formation period. The MYNN boundary layer scheme outperforms in near-surface wind simulation than other schemes. Combination of RUC land surface scheme and MYNN boundary layer scheme best simulates and reproduces the near-surface atmospheric conditions. Inter-comparison on the dynamic and thermodynamic of near-surface atmosphere among the experiments indicates that the simulation discrepancies are mainly in near-surface atmospheric stability, which could be the key factor in affecting the simulation performances. In particular, during the fog period, near-surface atmosphere is dominated by stable stratification, and the vertical velocity is relatively weak; before and after the fog period, the near-surface atmospheric stability is unstable.

Assessment of the Forecast Skill of Multiphysics and Multistochastic Methods within the GRAPES Regional Ensemble Prediction System in the East Asian Monsoon Region

Abstract To more comprehensively and accurately address model uncertainties in the East Asia monsoon region, a single-physics suite, where each ensemble member uses the same set of physics parameterizations as the control member in combination with multiple stochastic schemes, is developed to investigate if the multistochastic schemes that combine different stochastic schemes together can be an alternative to a multiphysics suite, where each ensemble member uses a different set of physics parameterizations (e.g., cumulus convection, boundary layer, surface layer, microphysics, and shortwave and longwave radiation). For this purpose, two experiments are performed for a summer monsoon month over China: one with a multiphysics suite and the other with a single-physics suite combined with multistochastic schemes. Three stochastic schemes are applied: the stochastically perturbed parameterizations (SPP) scheme, consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes; the stochastically perturbed parameterization tendencies (SPPT) scheme; and the stochastic kinetic energy backscatter (SKEB) scheme. The combination of the three stochastic schemes is compared with the multiphysics suite in the Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System with a horizontal grid spacing of 15 km. Verification results show that, overall, a single-physics suite that combines SPP, SPPT, and SKEB outperforms the multiphysics suite in precipitation verification and verification for upper-air weather variables, 10-m zonal wind, and 2-m temperature in the East Asian monsoon region. The indication is that a single-physics suite combining SPP, SPPT, and SKEB may be an appropriate alternative to a multiphysics suite. This finding lays a foundation for the development and design of future regional and global ensembles.

Evaluation of Monin–Obukhov and Bulk Richardson Parameterizations for Surface–Atmosphere Exchange

AbstractThe Land–Atmosphere Feedback Experiment (LAFE) was a field campaign to investigate influences of different land surface types on the atmospheric boundary layer (ABL). The primary goals of LAFE were to better understand ABL development and structure and to improve turbulence parameterizations in numerical weather prediction models. Three 10-m micrometeorological towers were installed over different land surface types (i.e., early growth soybean, native grassland, and mature soybean) along a 1.7-km southwest–northeast-oriented line. All towers measured standard meteorological variables in addition to heat, moisture, and momentum fluxes. In this study, we used these measurements to evaluate the validity of applying Monin–Obukhov similarity theory (MOST) to represent surface–atmosphere exchange over different land surface types. We investigated relationships between stability length ζ and the dimensionless wind shear ϕm, temperature gradient ϕh, and moisture gradient ϕq as well as relationships between bulk Richardson number Rib, friction coefficient Cu, heat-transfer coefficient Ct, and moisture-transfer coefficient Cr. We evaluated the new similarity functions developed using independent datasets obtained during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE). We found that using the Rib functions rather than the more traditional ζ functions to compute wind, temperature, and moisture yielded better agreement with the VORTEX-SE observations. These findings underscore limitations in MOST and motivate the need to consider modifying the functional forms of the similarity equations that form the basis for surface-layer parameterizations in numerical weather prediction models.

Evaluation of Noah-MP Land-Model Uncertainties over Sparsely Vegetated Sites on the Tibet Plateau

The water budget and energy exchange over the Tibetan Plateau (TP) region play an important role on the Asian monsoon. However, it is not well presented in the current land surface models (LSMs). In this study, uncertainties in the Noah with multiparameterization (Noah-MP) LSM are assessed through physics ensemble simulations in three sparsely vegetated sites located in the central TP. The impact of soil organic matter on energy flux and water cycles, along with the influence of uncertainties in precipitation are explored using observations at those sites during the third Tibetan Plateau Experiment from 1August2014 to31July2015. The greatest uncertainties are in the subprocesses of the canopy resistance, soil moisture limiting factors for evaporation, runoff (RNF) and ground water, and surface-layer parameterization. These uncertain subprocesses do not change across the different precipitation datasets. More precipitation can increase the annual total net radiation (Rn), latent heat flux (LH) and RNF, but decrease sensible heat flux (SH). Soil organic matter enlarges the annual total LH by ~26% but lessens the annual total Rn, SH, and RNF by ~7%, 7%, and 39%, respectively. Its effect on the LH and RNF at the Nagqu site, which has a sand soil texture type, is greater than that at the other two sites with sandy loam. This study highlights the importance of precipitation uncertainties and the effect of soil organic matter on the Noah-MP land-model simulations. It provides a guidance to improve the Noah-MP LSM further and hence the land-atmosphere interactions simulated by weather and climate models over the TP region.

Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

To represent model uncertainties more comprehensively, a stochastically perturbed parameterization (SPP) scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes, as well as the stochastically perturbed parameterization tendencies (SPPT) scheme, and the stochastic kinetic energy backscatter (SKEB) scheme, is applied in the Global and Regional Assimilation and Prediction Enhanced System—Regional Ensemble Prediction System (GRAPES-REPS) to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes. Six experiments are performed for a summer month (1–30 June 2015) over China and multiple verification metrics are used. The results show that: (1) All stochastic experiments outperform the control (CTL) experiment, and all combinations of stochastic parameterization schemes perform better than the single SPP scheme, indicating that stochastic methods can effectively improve the forecast skill, and combinations of multiple stochastic parameterization schemes can better represent model uncertainties; (2) The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill; (3) Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed. SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields, and it contributes most to the improvement in spread and outliers for wind; (4) The introduction of SPP has a positive added value, and does not lead to large changes in the evolution of the kinetic energy (KE) spectrum at any wavelength; (5) The introduction of SPPT and SKEB would cause a 5%–10% and 30%–80% change in the KE of mesoscale systems, and all three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the KE of mesoscale systems. This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.