Set Of Parameterizations Research Articles

Effect of waves on the magnitude and direction of wind stress over the Ocean

Correctly estimating the wind stress at the sea surface is of the utmost importance in models for climate studies, weather forecasting, and ocean–atmosphere interaction. The wind stress is mainly obtained by drag coefficient parameterizations, which always consider the wind stress to be aligned with the wind, but this is sometimes the case. Also, during moderate to weak wind conditions, these parameterizations may lead to high estimation errors due to the presence of swell. This study measured the wind stress with a high-rate (100 Hz) sonic anemometer mounted on a spar buoy. The sea state was also characterized by obtaining the directional spectrum of the waves by six wave-staff arrays sensing the free surface level at 10 Hz. Bouy’s movement was corrected by employing an inertial motion unit. The turbulent and wave-coherent wind stress components were also estimated and analyzed. It was observed that during swell conditions with wind traveling in the same direction, the wave-coherent wind stress component has an opposite direction to the wind and dampens the total wind stress magnitude. During counter-directional wind relative to swell events, the wave boundary layer is modified; swell produces a wave-coherent wind stress in the same direction as the wind, resulting in an enhanced total wind stress magnitude. The wave age, significant wave height, and the traveling direction of the swell relative to the wind are essential to correctly estimating the wind stress in swell-dominant conditions. A set of empirical parameterizations for each wind stress component is proposed.

Mass property estimation on TSE(3) via unscented Kalman filter using RCS thrusters

Estimation of spacecraft mass properties in the presence of noise is a nontrivial problem that, when properly addressed, can substantially reduce the crew time devoted to cargo management, control effort expended to manage fuel mass, or both. The estimation of such properties is treated here using a dual unscented Kalman filter (UKF) where the dynamics of the rigid-body spacecraft are formulated on the special Euclidean group SE(3) and its tangent bundle TSE(3) to avoid singularity and nonuniqueness issues found in attitude parameterization sets. The dual UKF on TSE(3) is developed, and a specific methodology is proposed for the estimation of the constant mass properties. An excitation approach is employed in the mass property estimation scheme. Arbitrary sensor suite placements are considered and implemented in the measurement model to capture real-world spacecraft behavior. Furthermore, a reaction control system with duty cycle constraints and noisy thrust values is implemented to account for uncertainty in the excitation inputs. With these features, the methods contained herein are found to be effective at estimating mass properties.

Evaluation of a High Resolution WRF Model for Southeast Brazilian Coast: The Importance of Physical Parameterization to Wind Representation

This study assesses the performance of the Weather Research and Forecasting (WRF) model using a high-resolution spatial grid (1 km) with various combinations of physical parameterization packages to simulate a severe event in August 2021 in the southeastern Brazilian coast. After determining the optimal set of physical parameterizations for representing wind patterns during this event, a year-long evaluation was conducted, covering forecast horizons of 24, 48, and 72 h. The simulation results were compared with observational wind data from four weather stations. The findings highlight variations in the efficacy of different physical parameterization sets, with certain sets encountering challenges in accurately depicting the peak of the severe event. The most favorable results were achieved using a combination of Tiedtke (cumulus), Thompson (microphysics), TKE (boundary layer), Monin-Obukhov (surface layer), Unified-NOAH (land surface), and RRTMG (shortwave and longwave radiation). Over the one-year forecasting period, the WRF model effectively represented the overall wind pattern, including forecasts up to three days in advance (72-h forecast horizon). Generally, the statistical metrics indicate robust model performance, even for the 72-h forecast horizon, with correlation coefficients consistently exceeding 0.60 at all analyzed points. While the model proficiently captured wind distribution, it tended to overestimate northeast wind speed and gust intensities. Notably, forecast accuracy decreased as stations approached the ocean, exemplified by the ATPM station.

The implementation of dust mineralogy in COSMO5.05-MUSCAT

Abstract. Mineral dust aerosols are composed of a complex assemblage of various minerals depending on the region in which they originated. Given the different mineral composition of desert dust aerosols, different physicochemical properties and therefore varying climate effects are expected. Despite the known regional variations in mineral composition, chemical transport models typically assume that mineral dust aerosols have uniform composition. This study adds, for the first time, mineralogical information to the mineral dust emission scheme used in the chemical transport model COSMO–MUSCAT. We provide a detailed description of the implementation of the mineralogical database, GMINER (Nickovic et al., 2012), together with a specific set of physical parameterizations in the model's mineral dust emission module, which led to a general improvement of the model performance when comparing the simulated mineral dust aerosols with measurements over the Sahara region for January–February 2022. The simulated mineral dust aerosol vertical distribution is tested by a comparison with aerosol lidar measurements from the lidar system PollyXT, located at Cape Verde. For a lofted mineral dust aerosol layer on 2 February at 05:00 UTC the lidar retrievals yield a dust mass concentration peak of 156 µg m−3, while the model calculates the mineral dust peak at 136 µg m−3. The results highlight the possibility of using the model with resolved mineral dust composition for interpretation of the lidar measurements since a higher absorption in the UV–Vis wavelengths is correlated with particles having a higher hematite content. Additionally, the comparison with in situ mineralogical measurements of dust aerosol particles shows that more of them are needed for model evaluation.

Reappraisal of the 1926, Jersey earthquake source parameters

The Cotentin peninsula is a region located on the northeastern edge of the Armorican Massif (France) that hosts several nuclear and radioactive waste facilities. A recent publication raised some issues regarding the source parameters of the 1926, Jersey earthquake, one of the key reference earthquakes for assessing the seismic hazard at the nuclear sites. In this study, we performed in-depth analyses on both the instrumental and macroseismic data to reappraise the source parameters. While the instrumental epicentral location was fairly well constrained at about mid-distance between the Jersey Island and the Cotentin peninsula, the focal depth could not be retrieved due to the resolution limits associated with a sparse seismological network and to the uncertainties on the phase picking. Different MS formulations applicable for moderate magnitude events recorded at regional distances were implemented to better account for the epistemic uncertainties, resulting in an estimation of MS equal to 5.1 ± 0.3. Regarding the moment magnitude MW, we performed a broad series of waveform modeling accounting for a broad set of source parameterizations and signal processing techniques. The magnitude MW of the Jersey earthquake is estimated to 5.4 ± 0.3 although these evaluations are strongly influenced by the period range adopted to fit the observations. We also carried out the evaluation of both MS and MW magnitudes by modeling the macroseismic field with results (MS = 5.1 ± 0.3; Mw = 5.3 ± 0.3) that are consistent with the instrumental estimates. Regarding the focal depth, the macroseismic analyses shed light on this parameter, even though the results depend on the assumption made on the presumed epicentral intensity (∼18 km for I0 = VI-VII and ∼ 12 km for I0 = VII).

Puzzles of Surface Segregation in Binary Pt–Pd Nanoparticles: Molecular Dynamics and Thermodynamic Simulations

Up till now, there have been extremely contradictory opinions and inadequate results concerning surface segregation in binary platinum–palladium (Pt–Pd) nanoparticles, including the problems regarding segregating components, as well as the size and temperature dependences of segregation. Taking into account such a situation, we investigated the surface segregation in Pt–Pd nanoparticles by combining atomistic (molecular dynamics) and thermodynamic simulations. For molecular dynamics experiments, the well-known program LAMMPS and the embedded atom method were employed. In the course of the atomistic simulations, two different sets of parameterizations for the Pt–Pt, Pd–Pd, and Pt–Pd interatomic interaction potentials were used. The thermodynamic simulation was based on solving the Butler equation by employing several successive approximations. The results obtained via atomistic simulation and thermodynamic simulation on the basis of the Butler equation were compared with each other, as well as with predictions that were based on the Langmuir–McLean equation and some experimental data. Both simulation methods (atomistic and thermodynamic) predicted the surface segregation of Pd, which diminishes with the nanoparticle size and with increasing temperature. Our simulation results do not confirm the predictions of some authors on surface segregation inversion, i.e., the reversal from the surface segregation of Pd to the surface segregation of Pt when diminishing the nanoparticle size.

Local optimization of low power laser machining of MgO at the green state enables cost- and energy-efficient benchtop fabrication

Laser machining of ceramics at the green state offers a low-cost route to the near-net fabrication of ceramics. A matrix of laser machining (i.e., laser cutting and scanning) parameters were tested on 5 mm-thick green bodies of magnesium oxide (MgO) using a CO2 laser with a maximum power capacity of 130 W. The input parameters were feed rate and power; we scanned the feed rate of 3–14 mm/s and power of 23–31 W for laser cutting, and 10–60 mm/s and 16–27 W for laser scanning. The quality of the laser-cut and -scanned surfaces was tracked through SEM. Thermal damage along the laser path was assessed to optimize laser cutting. High laser power and/or long dwell time resulted in partial sintering. At both cutting and scanning operations, low feed rates induced thermal damage, while high feed rates provided better edge retention and surface smoothness. However, very high feed rates failed to generate complete cuts and through-holes due to insufficient transfer of the excitation energy. Various combinations of the feed rate and power produced completely cut 5 mm cubes with smooth surfaces via laser cutting in a single pass. The most energy-efficient combination of 12 mm/s and 28 W rendered the highest surface quality with minimum thermal damage. This parameterization set provides a practical roadmap for choosing process parameters for the laser machining of MgO at the green state.

Investigation of unique Arabian Sea tropical cyclone with GPU-based WRF model: A case study of Shaheen

The recent severe cyclonic storm (SCS) Shaheen was a rare and unique Tropical Cyclone (TC) formed on September 30, 2021, on the Saurashtra coast off Gujrat, from a depression remnant of another cyclone Gulab off the Bay of Bengal. Under the influence of warm waters, this unique storm travelled westwards, skirting Pakistan's Makran coast before making landfall on northern Oman on October 4, 2021. The present study attempts to understand cyclone Shaheen's track, intensity, and synoptic features with the GPU-based WRF-ARW (v-3.8.1) on the google cloud platform (GCP). A 9 km resolution model domain covering the cyclone's track is initiated with an 18 UTC cycle of the Global Forecasting System (GFS) data. The model skilfully captured Shaheen's track and intensity up to 24 h of lead time which was in close agreement with the India Meteorological Department (IMD) estimates. The simulated track showed an overall track error of approximately 73 km compared to IMD. However, the surface wind stress was in close agreement with IMD. The spatial-temporal analysis of the simulated maximum surface wind speed showed the formation of the distinct eye of the cyclone moving away from the Indian coast towards the Gulf of Oman. Results indicated that Shaheen had a strong low-level (850 hPa) relative vorticity of approximately 193.56 10-5s-1 on 2nd October during the matured severe cyclonic stage, which got reduced to 77.88 × 10-5s-1 on 4th October as the system weakened. Comparison of the simulated synoptic parameters of the storm with reanalysis datasets ERA-5 and NGFS datasets indicated that the model simulated a stronger TC. The cyclone system showed significant relative humidity of about 80–90% with the well-delineated eyewall throughout various stages. The study shows the GPU-based WRF model's reliability with the current set of parameterizations for investigating a unique TC, Shaheen, on a public cloud infrastructure.

Ab initio symmetry-adapted emulator for studying emergent collectivity and clustering in nuclei

We discuss emulators from the ab initio symmetry-adapted no-core shell-model framework for studying the formation of alpha clustering and collective properties without effective charges. We present a new type of an emulator, one that utilizes the eigenvector continuation technique but is based on the use of symplectic symmetry considerations. This is achieved by using physically relevant degrees of freedom, namely, the symmetry-adapted basis, which exploits the almost perfect symplectic symmetry in nuclei. Specifically, we study excitation energies, point-proton root-mean-square radii, along with electric quadrupole moments and transitions for 6Li and 12C. We show that the set of parameterizations of the chiral potential used to train the emulators has no significant effect on predictions of dominant nuclear features, such as shape and the associated symplectic symmetry, along with cluster formation, but slightly varies details that affect collective quadrupole moments, asymptotic normalization coefficients, and alpha partial widths up to a factor of two. This makes these types of emulators important for further constraining the nuclear force for high-precision nuclear structure and reaction observables.

Spectral wave modeling of bimodal sea states at laboratory and coastal scales

A downshift of the wind wave peak frequency was observed in a wind wave tunnel when irregular long paddle-waves (i.e. generated mechanically with a plane wave-maker) are added in the sea state. The 3rd generation spectral wave model, TOMAWAC, is used to assess the extent at which this peak frequency downshift can take place at prototype scale in bimodal sea-state conditions involving swell and wind wave systems. Several parameterizations of the modeled physical processes are selected to numerically reproduce the laboratory experiments in the first place. Then, the model performances are further inquired in reproducing coastal observations during a specific event combining a wind wave and a swell system. Overall, a good agreement is obtained between the simulations and the observations both at laboratory and coastal scale. In particular, a set of parameterizations combining one of the latest developments in spectral wave models for the whitecapping dissipation and the nonlinear 4-wave interactions reveals high performances in reproducing the observations. Lastly, based on the performances of this latter set of parameterizations, a generic numerical domain with typical coastal scale dimensions is created to inquire the occurrence of the downshift at prototype scale. This last study reveals a wind wave peak period shift from 5 s without swell to more than 6.5 s with a 2 m high swell.

Constraining Clouds and Convective Parameterizations in a Climate Model Using Paleoclimate Data

AbstractCloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof‐of‐concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere‐only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present‐day runs using multiple satellite climatologies and by comparing simulated δ18O of precipitation (δ18Op), known to be sensitive to parameterization schemes, with a global database of speleothem δ18O records covering the Last Glacial Maximum (LGM), mid‐Holocene (MH) and pre‐industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ18Op values across all time periods, along with LGM and MH δ18Op anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non‐stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations.

Novel Parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals

Abstract Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter De, that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud De has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of De as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds. Significance Statement We derived unique parameterizations of ice crystal effective size from global satellite measurements in an effort to more robustly and consistently represent ice clouds in numerical models for weather forecasting and climate energy balance studies. Based on our results, effective ice crystal size is easily solved based on temperature and visible cloud translucence. By knowing the size of the ice crystals, we can then estimate cloud scattering and absorption. In comparison with aircraft-based parameterizations, the satellite data reveal that ice crystal effective sizes are much smaller, on global average, for ice clouds occurring in relatively warm layers (>230 K), indicating that many ice clouds are more reflective than previously believed.

Impacts of Land Surface Parameterizations on Simulations over the Arid and Semiarid Regions: The Case of the Loess Plateau in China

Abstract The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations.

ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic

Abstract. ECOSMO II is a fully coupled bio-physical model of 3D hydrodynamics with an intermediate-complexity NPZD (nutrient, phytoplankton, zooplankton, detritus) type biology including sediment-water column exchange processes originally formulated for the North Sea and Baltic Sea. Here we present an updated version of the model incorporating chlorophyll a as a prognostic state variable: ECOSMO II(CHL). The version presented here is online coupled to the HYCOM ocean model. The model is intended to be used for regional configurations for the North Atlantic and the Arctic incorporating coarse to high spatial resolutions for hind-casting and operational purposes. We provide the full descriptions of the changes in ECOSMO II(CHL) from ECOSMO II and provide the evaluation for the inorganic nutrients and chlorophyll a variables, present the modelled biogeochemistry of the Nordic Seas and the Arctic, and experiment on various parameterization sets as use cases targeting chlorophyll a dynamics. We document the performance of each parameter set objectively analysing the experiments against in situ, satellite and climatology data. The model evaluations for each experiment demonstrated that the simulations are consistent with the large-scale climatological nutrient setting and are capable of representing regional and seasonal changes. Explicitly resolving chlorophyll a allows for more dynamic seasonal and vertical variations in phytoplankton biomass to chlorophyll a ratio and improves model chlorophyll a performance near the surface. Through experimenting with the model performance, we document the general biogeochemisty of the Nordic Seas and the Arctic. The Norwegian and Barents seas primary production show distinct seasonal patterns with a pronounced spring bloom dominated by diatoms and low biomass during winter months. The Norwegian Sea annual primary production is around double that of the Barents Sea while also having an earlier spring bloom.

Employing Spectral Analysis to Obtain Dispersion Parameters in an Atmospheric Environment Driven by a Mesoscale Downslope Windstorm

Considering the influence of the downslope windstorm called “Vento Norte” (VNOR; Portuguese for “North Wind”) in planetary boundary layer turbulent features, a new set of turbulent parameterizations, which are to be used in atmospheric dispersion models, has been derived. Taylor’s statistical diffusion theory, velocity spectra obtained at four levels (3, 6, 14, and 30 m) in a micrometeorological tower, and the energy-containing eddy scales are used to calculate neutral planetary boundary layer turbulent parameters. Vertical profile formulations of the wind velocity variances and Lagrangian decorrelation time scales are proposed, and to validate this new parameterization, it is applied in a Lagrangian Stochastic Particle Dispersion Model to simulate the Prairie Grass concentration experiments. The simulated concentration results were shown to agree with those observed.

The Diurnal Cycle of Winter Season Temperature Errors in the Operational Global Forecast System (GFS)

AbstractForecasts from NOAA's Global Forecast System (GFS) and the High‐Resolution Rapid Refresh (HRRR) weather models are matched to surface observations for the winter season of November 2019 to March 2020 at 210 airports across the United States. The 2‐m temperature errors, conditioned on observed weather conditions such as cloud cover amount and wind speed, are used to determine the nature of systematic model biases. We observe a strong diurnal cycle in 2‐m temperature errors in the GFS in conditions with 50% and 25% sky cover, with a 1°C warm bias at night and a 2°C cold bias during the day. The HRRR, which uses a different set of physical parameterizations, does not have a clear diurnal cycle in errors under the same conditions. These results highlight the utility of weather‐conditional comparisons across the diurnal cycle to diagnose sources of model weaknesses and to target model improvements.

Symbol alphabets from plabic graphs III: n = 9

Symbol alphabets of n-particle amplitudes in mathcal{N} = 4 super-Yang-Mills theory are known to contain certain cluster variables of G(4, n) as well as certain algebraic functions of cluster variables. In this paper we solve the C Z = 0 matrix equations associated to several cells of the totally non-negative Grassmannian, combining methods of arXiv:2012.15812 for rational letters and arXiv:2007.00646 for algebraic letters. We identify sets of parameterizations of the top cell of G+(5, 9) for which the solutions produce all of (and only) the cluster variable letters of the 2-loop nine-particle NMHV amplitude, and identify plabic graphs from which all of its algebraic letters originate.

Simulated or measured soil moisture: which one is adding more value to regional landslide early warning?

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWSs). However, it is still unclear which source of information – numerical models or in situ measurements – is of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019, using a physically based 1D soil moisture transfer model. A common parameterization set was defined for all sites, except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and receiver operating characteristic analysis (ROC) was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different model parameterizations, to various distances between simulation sites and landslides and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes, and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

Impact of Sea‐Ice Model Complexity on the Performance of an Unstructured‐Mesh Sea‐Ice/Ocean Model under Different Atmospheric Forcings

AbstractWe have equipped the unstructured‐mesh global sea‐ice and ocean model FESOM2 with a set of physical parameterizations derived from the single‐column sea‐ice model Icepack. The update has substantially broadened the range of physical processes that can be represented by the model. The new features are directly implemented on the unstructured FESOM2 mesh, and thereby benefit from the flexibility that comes with it in terms of spatial resolution. A subset of the parameter space of three model configurations, with increasing complexity, has been calibrated with an iterative Green's function optimization method to test the impact of the model update on the sea‐ice representation. Furthermore, to explore the sensitivity of the results to different atmospheric forcings, each model configuration was calibrated separately for the NCEP‐CFSR/CFSv2 and ERA5 forcings. The results suggest that a complex model formulation leads to a better agreement between modeled and the observed sea‐ice concentration and snow thickness, while differences are smaller for sea‐ice thickness and drift speed. However, the choice of the atmospheric forcing also impacts the agreement of the FESOM2 simulations and observations, with NCEP‐CFSR/CFSv2 being particularly beneficial for the simulated sea‐ice concentration and ERA5 for sea‐ice drift speed. In this respect, our results indicate that parameter calibration can better compensate for differences among atmospheric forcings in a simpler model (i.e., sea‐ice has no heat capacity) than in more realistic formulations with a prognostic sea‐ice thickness distribution and sea ice enthalpy.

Size-resolved atmospheric ice-nucleating particles during East Asian dust events

Abstract. Asian dust is an important source of atmospheric ice-nucleating particles (INPs). However, the freezing activity of airborne Asian dust, especially its sensitivity to particle size, is poorly understood. In this study we report the first INP measurement of size-resolved airborne mineral dust collected during East Asian dust events. The measured total INP concentrations in the immersion mode ranged from 10−2 to 102 L−1 in dust events at temperatures between −25 and −5 ∘C. The average contributions of heat-sensitive INPs at three temperatures, −10, −15, and −20 ∘C, were 81±12 %, 70±15 %, and 38±21 %, respectively, suggesting that proteinaceous biological materials have a substantial effect on the ice nucleation properties of Asian airborne mineral dust at high temperatures. The dust particles which originated from China's northwest deserts are more efficient INPs compared to those from northern regions. In general, there was no significant difference in the ice nucleation properties between East Asian dust particles and other regions in the world. An explicit size dependence of both INP concentration and surface ice-active-site density was observed. The nucleation efficiency of dust particles increased with increasing particle size, while the INP concentration first increased rapidly and then leveled, due to the significant decrease in the number concentration of larger particles. A new set of parameterizations for INP activity based on size-resolved nucleation properties of Asian mineral dust particles were developed over an extended temperature range (−35 to −6 ∘C). These size-dependent parameterizations require only particle size distribution as input and can be easily applied in models.