Microphysical Parameters Research Articles

Shortwave Infrared Multi-Angle Polarization Imager (MAPI) Onboard Fengyun-3 Precipitation Satellite for Enhanced Cloud Characterization

Accurate measurement of the radiative properties of clouds and aerosols is of great significance to global climate change and numerical weather prediction. The multi-angle polarization imager (MAPI) onboard the Fengyun-3 precipitation satellite, planned to be launched in 2023, will provide the multi-angle, multi-shortwave infrared (SWIR) channels and multi-polarization satellite observation of clouds and aerosols. MAPI operates in a non-sun-synchronized inclined orbit and provides images with a spatial resolution of 3 km (sub-satellite) and a swath of 700 km. The observation channels of the MAPI include 1030 nm, 1370 nm, and 1640 nm polarization channels and corresponding non-polarization channels, which provide observation information from 14 angles. In-flight radiometric and polarimetric calibration strategies are introduced, aiming to achieve radiometric accuracy of 5% and polarimetric accuracy of 2%. Simulation experiments show that the MAPI has some unique advantages of characterizing clouds and aerosols. For cloud observation, the polarization phase functions of the 1030 nm and 1640 nm around the scattering angle of a cloudbow show strong sensitivity to cloud droplet radius and effective variance. In addition, the polarized observation of the 1030 nm and 1640 nm has a higher content of information for aerosol than VIS-NIR. Additionally, the unique observation geometry of non-sun-synchronous orbits can provide more radiometric and polarization information with expanded scattering angles. Thus, the multi-angle polarization measurement of the new SWIR channel onboard Fengyun-3 can optimize cloud phase state identification and cloud microphysical parameter inversion, as well as the retrieval of aerosols. The results obtained from the simulations will provide support for the design of the next generation of polarized imagers of China.

The influence of multiple groups of biological ice nucleating particles on microphysical properties of mixed-phase clouds observed during MC3E

Abstract. A new empirical parameterization (EP) for multiple groups of primary biological aerosol particles (PBAPs) is implemented in the aerosol–cloud model (AC) to investigate their roles as ice nucleating particles (INPs). The EP describes the heterogeneous ice nucleation by (1) fungal spores, (2) bacteria, (3) pollen, (4) detritus of plants, animals, and viruses, and (5) algae. Each group includes fragments from the originally emitted particles. A high-resolution simulation of a midlatitude mesoscale squall line by AC is validated against airborne and ground observations. Sensitivity tests are carried out by varying the initial vertical profiles of the loadings of individual PBAP groups. The resulting changes in warm and ice cloud microphysical parameters are investigated. The changes in warm microphysical parameters, including liquid water content and cloud droplet number concentration, are minimal (<10 %). Overall, PBAPs have little effect on the ice number concentration (<6 %) in the convective region. In the stratiform region, increasing the initial PBAP loadings by a factor of 1000 resulted in less than 40 % change in ice number concentrations. The total ice concentration is mostly controlled by various mechanisms of secondary ice production (SIP). However, when SIP is intentionally shut down in sensitivity tests, increasing the PBAP loading by a factor of 100 has an effect of less than 3 % on the ice phase. Further sensitivity tests revealed that PBAPs have little effect on surface precipitation and on the shortwave and longwave flux (<4 %) for a 100-fold perturbation in PBAPs.

Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)

Abstract. The Weather Research and Forecasting (WRF) model offers a multitude of physics parameterizations to study and analyze the different atmospheric processes and dynamics that are observed in the Earth's atmosphere. However, the suitability of a WRF model setup is known to be highly sensitive to the type of weather phenomena and the type and combination of physics parameterizations. A multi-event sensitivity analysis is conducted to identify general trends and suitable WRF physics setups for three extreme weather events identified to be potentially harmful for the operation and maintenance of wind farms located in the Belgian offshore concession zone. The events considered are Storm Ciara on 10 February 2020, a low-pressure system on 24 December 2020, and a trough passage on 27 June 2020. A total of 12 WRF simulations per event are performed to study the effect of the update interval of lateral boundary conditions and different combinations of physics parameterizations (planetary boundary layer, PBL; cumulus; and microphysics). Specifically, the update interval of ERA5 lateral boundary conditions is varied between hourly and 3-hourly. Physics parameterizations are varied between three PBL schemes (Mellor–Yamada–Nakanishi–Niino, MYNN; scale-aware Shin-Hong; and scale-aware Zhang), four cumulus schemes (Kain–Fritsch, Grell–Dévényi, scale-aware Grell–Freitas, and multi-scale Kain–Fritsch), and three microphysics schemes (WRF Single-Moment five-class scheme, WSM5; Thompson; and Morrison). The simulated wind direction and wind speed are compared qualitatively and quantitatively to operational supervisory control and data acquisition (SCADA) data. Overall, a definitive best-case setup common to all three events is not identified in this study. For wind direction and wind speed, the best-case setups are identified to employ scale-aware PBL schemes. These are most often driven by hourly update intervals of lateral boundary conditions as opposed to 3-hourly update intervals, although it is only in the case of Storm Ciara that significant differences are observed. Scale-aware cumulus schemes are identified to produce better results when combined with scale-aware PBL schemes, specifically for Storm Ciara and the trough passage cases. However, for the low-pressure-system case this trend is not observed. No clear trend in utilizing higher-order microphysics parameterization considering the combinations of WRF setups in this study is found in all cases. Overall, the combination of PBL, cumulus, and microphysics schemes is found to be highly sensitive to the type of extreme weather event. Qualitatively, precipitation fields are found to be highly sensitive to model setup and the type of weather phenomena.

Measuring the inflaton coupling in the CMB

We study the perspectives to extract information about the microphysical parameters that governed the reheating process after cosmic inflation from CMB data. We identify conditions under which the inflaton coupling to other fields can be constrained for a given model of inflation without having to specify the details of the particle physics theory within which this model is realised. This is possible when the effective potential during reheating is approximately parabolic, and when the coupling constants are smaller than an upper bound that is determined by the ratios between the inflaton mass and the Planck mass or the scale of inflation. We consider scalar, Yukawa, and axion-like interactions and estimate that these conditions can be fulfilled if the inflaton coupling is comparable to the electron Yukawa coupling or smaller, and if the inflaton mass is larger than 105 GeV. Constraining the order of magnitude of the coupling constant requires measuring the scalar-to-tensor ratio at the level of 10-3, which is possible with future CMB observatories. Such a measurement would provide an important clue to understand how a given model of inflation may be embedded into a more fundamental theory of nature.

Microphysical Perturbation Experiments and Ensemble Forecasts on Summertime Heavy Rainfall over Northern Taiwan

Abstract Microphysical perturbation experiments were conducted to investigate the sensitivity of convective heavy rain simulation to cloud microphysical parameterization and its feasibility for ensemble forecasts. An ensemble of 20 perturbation members differing in either the microphysics package or process treatments within a single scheme was applied to simulate 10 summer-afternoon heavy-rain convection cases. The simulations revealed substantial disagreements in the location and amplitude of peak rainfall among the microphysics-package and single-scheme members, with an overall spread of 57%–161%, 66%–161%, and 65%–149% of the observed average rainfall, maximum rainfall, and maximum intensity, respectively. The single-scheme members revealed that the simulation of heavy convective precipitation is quite sensitive to factors including ice-particle fall speed parameterization, aerosol type, ice particle shape, and size distribution representation. The microphysical ensemble can derive reasonable probability of occurrence for a location-specific heavy-rain forecast. Spatial-forecast performance indices up to 0.6 were attained by applying an optimal fuzzy radius of about 8 km for the warning-area coverage. The forecasts tend to be more successful for more organized convection. Spectral mapping methods were further applied to provide ensemble forecasts for the 10 heavy rainfall cases. For most cases, realistic spatial patterns were derived with spatial correlation up to 0.8. The quantitative performance in average rainfall, maximum rainfall, and maximum intensity from the ensembles reached correlations of 0.83, 0.84, and 0.51, respectively, with the observed values. Significance Statement Heavy rainfall from summer convections is stochastic in terms of intensity and location; therefore, an accurate deterministic forecast is often challenging. We designed perturbation experiments to explore weather forecasting models’ sensitivity to cloud microphysical parameterizations and the feasibility of application to ensemble forecast. Promising results were obtained from simulations of 10 real cases. The cloud microphysical ensemble approach may provide reasonable forecasts of heavy rainfall probability and convincing rainfall spatial distribution, particularly for more organized convection.

Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Cloud and Precipitation Forecasts

In this study, based on the retrieval data from the Fengyun geostationary meteorological satellite and the Tropical Rainfall Measuring Mission satellite, a large-scale precipitation case in eastern China is selected to address the systematic deviations of deterministic forecasts for clouds and precipitation. A multi-physical ensemble transform Kalman filter (ETKF) is constructed in this research based on the Weather Research and Forecast model version 3.6, and its forecasting ability in terms of cloud-top height and temperature, hydrometeors, and precipitation is evaluated by quantitatively comparing three microphysical parameterization schemes (Lin, Morrison, and CAM5.1 schemes) and their corresponding multi-physical ensemble mean. The results show that the Lin, Morrison, and CAM5.1 schemes all underestimate the range of cloud systems and have different advantages and disadvantages in forecasting different elements, while the forecasting improvement of the multi-physical ensemble mean is limited. However, the multi-physical ETKF can effectively improve the forecast accuracy of the cloud system range. In addition, the multi-physical ETKF has the advantages of different physical parameterization schemes, which can dramatically improve the forecast accuracy of cloud hydrometeors, reduce precipitation forecast errors, and improve threat scores.

Direct Assimilation of Radar Reflectivity Data Using Ensemble Kalman Filter Based on a Two-Moment Microphysics Scheme for the Analysis and Forecast of Typhoon Lekima (2019)

We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model for a landfall typhoon Lekima (2019). Observations from a single operational coastal Doppler are quality-controlled and assimilated. Compared with the baseline experiment initialized by GFS analysis, the reflectivity data assimilation experiment (Z-DA) resulted in an obvious improvement in both structural analysis and typhoon forecast skills in terms of intensity, precipitation, and track. Sensitivity experiments were conducted to evaluate the ability of EnKF to update certain state variables considering that the degree of freedom of analytical variables increased with a DM MP scheme. When either the total number concentration or other large-scale state variables that are not directly linked to reflectivity observations via the observation operator are not updated, the tendency of RMSIs and PS to be imbalanced is significantly increased during DA cycles compared to those of Z-DA with updating a full set of state variables, resulting in increased intensity and track forecast errors. These results indicate that the reliable ensemble covariance could handle the underconstraint issue associated with the DM scheme, and helps in obtaining more physically balanced analytical fields.

Evaluation of bulk microphysics parameterizations for simulating the vertical structure of heavy rainfall between Korea and the United States

The Korean Peninsula and the United States (US) have many contrasting precipitation characteristics that are part of the oceanic monsoon and continental convection. This study evaluated 11 microphysics schemes in the Weather Research and Forecasting model in terms of vertical reflectivity structure for heavy rainfall during the summer in Korea and the US. Compared to satellite radar observation, the correlation coefficients for the reflectivity structure for 11 microphysics schemes were 0.581–0.906 and 0.745–0.918 for the Korean and US domains, respectively. We observed that a simple scheme (such as the use of single moment and limited ice categories) does not always fail, and complex methods do not always succeed. The relatively low correlation over Korea versus the US was assumed because many microphysics parameterizations were developed and tuned over the US. Consequently, some microphysics schemes exhibited a severe overestimation of reflectivity in both the upper and lower levels over the Korean Peninsula. In contrast, a microphysics scheme that was evaluated and improved for both continental and maritime convection regions, showed the best performance on the Korean Peninsula too. This highlights the importance of evaluation for various regions with different precipitation characteristics to improve parameterization of cloud microphysics. Because Korean and US domains are representative areas of oceanic monsoon and continental convection, we expect that this study can serve as a conceptual guide to suggest which microphysics parameterizations should be used for a realistic simulation in continental and oceanic monsoon precipitation regimes.

Numerical Errors in Ice Microphysics Parameterizations and their Effects on Simulated Regional Climate

Numerical Errors in Ice Microphysics Parameterizations and their Effects on Simulated Regional Climate

Turbulence analysis on the flight of Etihad airways in Bangka Island using the WRF case study May 4, 2016

<p>Accurate weather forecasts should support the increase in safety of aviation operations in Indonesia. This weather forecast is needed, especially in detecting turbulence, considering that geographically Indonesia has effective solar radiation resulting in convective cloud formation. Convective clouds can trigger turbulence then produce disruption and even accidents on flights. This research uses a case study on the Etihad Airways flight on Bangka Island on May 4, 2016. At the time of the incident, there was turbulence at 39,000 feet altitude, and the aircraft did not enter the cloudy area. The Weather Research and Forecasting (WRF) model is used to simulate the turbulence in this study, which is downscaled up to 3 km with a microphysics parameterization of WRF Single Moment 6 Class (WSM6). The results were then verified using correlation and linear regression for temperature, wind direction, wind speed, and pattern resemblance between cloud fraction and the convective nuclei distribution. The turbulence is analyzed from the south-north and west-east vertical airflow. The turbulence spotted at 06.40 UTC when there is a quite strong updraft which can cause turbulence. The turbulence parameters used, such as the eddy dissipation rate (EDR) parameter, which has a value of 0.05 , Richardson number with a value of less than 0.25, and turbulence index (TI 1) with a maximum value of 4 x 10<sup>-7</sup> s<sup>-2</sup> found that turbulence was in a strong category. The turbulence that occurs in this study is identified as near cloud turbulence (NCT) event due to cloud formation observed in the west of the turbulence and intense updraft activity at the location of turbulence.</p>

A kriging-based analysis of cloud liquid water content using CloudSat data

Abstract. Spatiotemporal statistical learning has received increased attention in the past decade, due to spatially and temporally indexed data proliferation, especially data collected from satellite remote sensing. In the meantime, observational studies of clouds are recognized as an important step toward improving cloud representation in weather and climate models. Since 2006, the satellite CloudSat of NASA is carrying a 94 GHz cloud-profiling radar and is able to retrieve, from radar reflectivity, microphysical parameter distribution such as water or ice content. The collected data are piled up with the successive satellite orbits of nearly 2 h, leading to a large compressed database of 2 Tb (http://cloudsat.atmos.colostate.edu/, last access: 8 June 2022). These observations offer the opportunity to extend the cloud microphysical properties beyond the actual measurement locations using an interpolation and prediction algorithm. To do so, we introduce a statistical estimator based on the spatiotemporal covariance and mean of the observations known as kriging. An adequate parametric model for the covariance and the mean is chosen from an exploratory data analysis. Beforehand, it is necessary to estimate the parameters of this spatiotemporal model; this is performed in a Bayesian setting. The approach is then applied to a subset of the CloudSat dataset.

Coupling Warm Rain With an Eddy Diffusivity/Mass Flux Parameterization: 1. Model Description and Validation

AbstractA new version of the stochastic multiplume Jet Propulsion Laboratory Eddy‐Diffusivity/Mass‐Flux (JPL‐EDMF) parameterization which consistently couples the simplified Khairoutdinov and Kogan (2000), https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2, warm phase cloud microphysical parameterization with the parameterization of cloud macrophysical and subgrid scale dynamical processes is described. The new parameterization combines the EDMF approach with an assumed shape of a joint probability density function of thermodynamic and kinematic variables which provide the basis for the computation of all parameterized processes. As far as we are aware this is the first attempt to consistently couple all of these parameterized processes in the EDMF framework. This paper is part one of a two paper series. Here, the JPL‐EDMF parameterization is described and benchmark simulations of precipitating stratocumulus and cumulus convection are performed in a single‐column‐model framework. The parameterization results compare favorably to the reference large‐eddy‐simulation results. In the second part (Smalley et al., 2022, https://doi.org/10.1029/2021MS002729) the JPL‐EDMF parameterization is validated for a wide range of observation‐based scenarios covering the continuous transition from subtropical stratocumulus to cumulus convection derived from global reanalysis, and parameterization uncertainties are studied in detail.

VLBI observations of GRB 201015A, a relatively faint GRB with a hint of very high-energy gamma-ray emission

Context. A total of four long-duration gamma-ray bursts (GRBs) have been confirmed at very high-energy (≥100GeV) with high significance, and any possible peculiarities of these bursts will become clearer as the number of detected events increases. Multi-wavelength follow-up campaigns are required to extract information on the physical conditions within the jets that lead to the very high-energy counterpart, hence they are crucial to reveal the properties of this class of bursts. Aims. GRB 201015A is a long-duration GRB detected using the MAGIC telescopes from ~40 s after the burst. If confirmed, this would be the fifth and least luminous GRB ever detected at these energies. The goal of this work is to constrain the global and microphysical parameters of its afterglow phase, and to discuss the main properties of this burst in a broader context. Methods. Since the radio band, together with frequent optical and X-ray observations, proved to be a fundamental tool for overcoming the degeneracy in the afterglow modelling, we performed a radio follow-up of GRB 201015A over 12 different epochs, from 1.4 days (2020 October 17) to 117 days (2021 February 9) post-burst, with the Karl G. Jansky Very Large Array, e-MERLIN, and the European VLBI Network. We include optical and X-ray observations, performed respectively with the Multiple Mirror Telescope and the Chandra X-ray Observatory, together with publicly available data, in order to build multi-wavelength light curves and to compare them with the standard fireball model. Results. We detected a point-like transient, consistent with the position of GRB 201015A until 23 and 47 days post-burst at 1.5 and 5 GHz, respectively. No emission was detected in subsequent radio observations. The source was also detected in optical (1.4 and 2.2 days post-burst) and in X-ray (8.4 and 13.6 days post-burst) observations. Conclusions. The multi-wavelength afterglow light curves can be explained with the standard model for a GRB seen on-axis, which expands and decelerates into a medium with a homogeneous density. A circumburst medium with a wind-like profile is disfavoured. Notwithstanding the high resolution provided by the VLBI, we could not pinpoint any expansion or centroid displacement of the outflow. If the GRB is seen at the viewing angle θ that maximises the apparent velocity βapp (i.e. θ ~ βapp-1), we estimate that the Lorentz factor for the possible proper motion is Гα ≤ 40 in right ascension and Гδ ≤ 61 in declination. On the other hand, if the GRB is seen on-axis, the size of the afterglow is ≤5pc and ≤16pc at 25 and 47 days. Finally, the early peak in the optical light curve suggests the presence of a reverse shock component before 0.01 days from the burst.

Cloud radar perspective on tropical warm clouds associated with summer monsoon rainfall and warm rain onset process

The role of warm clouds in monsoon rainfall and the warm rain onset process are investigated using high-resolution cloud-sensitive radar measurements. Quality controlled data from Ka-band radar consisting of 1.8 million profiles during July–August 2015, have been explored. A method is devised using the Ze profiles, for identifying warm clouds whose cloud tops are confined below 5.3 km. Warm rain microphysical parameters are examined utilizing the complemented measurements from Disdrometer, rain gauge, and CloudSat data. The strong temperature inversion of 2 to 4 K km−1 at 3 km explained the generation of shallow warm clouds. Strong low-level updraft and subsidence above the warm clouds are also evident. Contoured frequency by altitude diagram infers that 75% of Ze (velocity) dominates at −20 dBZe (−1 m s−1). Simultaneous Disdrometer measurements revealed 78% of cases with zero-drop count and zero rain rates. The dominating non-precipitating characteristics of warm clouds are also confirmed from the CloudSat inferred cloud droplet effective radius below 10 μm. During the rainy episodes, peak at 0.91 mm in the drop size distribution suggests prominence of drizzle-sized drops. The precipitation caused by warm clouds accounts for a small fraction (28%) of the total rainfall. The warm rain onset process involving the transformation of smaller cloud droplets into big raindrops is investigated using the radar's three lower-order moments. Before cloud droplet to raindrop transition an intense spike in the spectral width (SW) is observed for a brief period in 98% of the cases. Therefore, SW, which infers turbulence, triggers the collision-coalescence of cloud droplets to form raindrops. This study provides a multi-observation-based perspective on the macro- and micro-physical characteristics of monsoon warm clouds and the onset of warm rain for a better representation in the global climate models.

Algorithmic Differentiation for Sensitivity Analysis in Cloud Microphysics

AbstractThe role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. The parameterization of cloud microphysical processes in weather and climate models is a well‐known source of uncertainties. Hence, robust quantification of this uncertainty is mandatory. Sensitivity analysis to date has typically investigated only a few model parameters. We propose algorithmic differentiation (AD) as a tool to detect the magnitude and timing at which a model state variable is sensitive to any of the hundreds of uncertain model parameters in the cloud microphysics parameterization. AD increases the computational cost by roughly a third in our simulations. We explore this methodology as the example of warm conveyor belt trajectories, that is, air parcels rising rapidly from the planetary boundary layer to the upper troposphere in the vicinity of an extratropical cyclone. Based on the information of derivatives with respect to the uncertain parameters, the ten parameters contributing most to uncertainty are selected. These uncertain parameters are mostly related to the representation of hydrometeor diameter and fall velocity, the activation of cloud condensation nuclei, and heterogeneous freezing. We demonstrate the meaningfulness of the AD‐estimated sensitivities by comparing the AD results with ensemble simulations spawned at different points along the trajectories, where different parameter settings are used in the various ensemble members. The ranking of the most important parameters from these ensemble simulations is consistent with the results from AD. Thus, AD is a helpful tool for selecting parameters contributing most to cloud microphysics uncertainty.

Seasonal Variation in Microphysical Characteristics of Precipitation at the Entrance of Water Vapor Channel in Yarlung Zangbo Grand Canyon

Mêdog is located at the entrance of the water vapor channel in the Yarlung Zangbo Grand Canyon (YGC). This area has the largest annual accumulated rainfall totals and precipitation frequency on the Tibetan Plateau (TP). This paper investigates the seasonal variation in raindrop size distribution (DSD) characteristics in Mêdog based on disdrometer observations from 1 July 2019 to 30 June 2020. The DSD characteristics are examined under six rain rate classes and two rainfall types (stratiform and convective) in the winter, premonsoon, monsoon and postmonsoon periods. The highest (lowest) concentration of small raindrops is observed in monsoon (winter) precipitation, whereas large raindrops predominate in premonsoon precipitation. For stratiform rainfall, the mean mass-weighted mean diameter (Dm) exhibits overlooked differences in the four periods, while the mean normalized intercept parameter (Nw) is significantly higher in the monsoon period than in the other three periods. The convective rainfall in the monsoon and postmonsoon periods is characterized by a high concentration of limited-size drops and can be classified as maritime-like. This is probably attributed to abundant warm and humid airflow transported by the Indian Ocean monsoon into Mêdog. The westerly winds prevail over the TP during the premonsoon period, and thereby the premonsoon convective rainfall in Mêdog has a larger mean Dm and a lower mean Nw. In addition, the relationships of radar reflectivity Z and rain rate R for different precipitation types in different periods are also derived. A better understanding of the seasonal variation in the microphysical characteristics of precipitation in Mêdog is important for improving the microphysical parameterization scheme and the precipitation forecast of models on the TP.

The diurnal cycle of the lightning potential index over the Mexican tropical continental region during tropical cyclone Bud

AbstractAtmospheric processes over the Mexican continental territory can be influenced by the occurrence of tropical cyclones (TCs) over the adjacent oceans. Furthermore, the Mexican territory is characterized by the presence of diurnal cycles of lightning. The Lightning Potential Index (LPI), that is a measure of the potential for charge generation and separation that leads to lightning production in convective storms, was assessed for the diurnal variability of lightning that exhibited a strong diurnal cycle over the Mexican continental territory when TC Bud was over the adjacent eastern Pacific Ocean. The assessment, from 0000 UTC 10 June to 2000 UTC 15 June 2018, used the Weather Research and Forecasting (WRF) model with a new hybrid terrain‐following sigma‐pressure vertical coordinate. Two ensembles with various cumulus and microphysical parameterizations were performed with a grid spacing of 2 km. In one ensemble, sea surface temperature (SST) was prescribed from the Real‐time global (RTG) SST analysis product and allowed to evolve interactively with the modeled atmosphere. Then, all the ensemble members were compared against available observations from the World Wide Lightning Location Network (WWLLN) to evaluate which model configurations perform best. It is not known if the LPI is capable of reproducing diurnal cycles of lightning over tropical regions; and the results allow gaining an understanding of the LPI when it reproduces the observed diurnal variability of lightning over land. The ensemble members that had better performances were those that included the prescribed SST.

A dataset of microphysical cloud parameters, retrieved from Fourier-transform infrared (FTIR) emission spectra measured in Arctic summer 2017

Abstract. A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were performed using a mobile Fourier-transform infrared (FTIR) spectrometer which was carried by RV Polarstern. The dataset contains retrieved optical depths and effective radii of ice and liquid water, from which the liquid water path and ice water path are calculated. The water paths and the effective radii retrieved from the FTIR measurements are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. The purpose of this comparison is to benchmark the infrared retrieval data against the established Cloudnet retrieval. For the liquid water path, the data correlate, showing a mean bias of 2.48 g m−2 and a root-mean-square error of 10.43 g m−2. It follows that the infrared retrieval is able to determine the liquid water path. Although liquid water path retrievals from the Cloudnet retrieval data come with an uncertainty of at least 20 g m−2, a root-mean-square error of 9.48 g m−2 for clouds with a liquid water path of at most 20 g m−2 is found. This indicates that the liquid water paths, especially of thin clouds, of the Cloudnet retrieval can be determined with higher accuracy than expected. Apart from this, the dataset of microphysical cloud properties presented here allows researchers to perform calculations of the cloud radiative effects when the Cloudnet data from the campaign are not available, which was the case from 22 July 2017 until 19 August 2017. The dataset is published at PANGAEA (https://doi.org/10.1594/PANGAEA.933829, Richter et al., 2021).

Large-Scale Saharan Dust Episode in April 2019: Study of Desert Aerosol Loads over Sofia, Bulgaria, Using Remote Sensing, In Situ, and Modeling Resources

Emissions of immense amounts of desert dust into the atmosphere, spreading over vast geographical areas, are in direct feedback relation with ongoing global climate changes. An extreme large-scale Saharan dust episode occurred over Mediterranean and Europe in April 2019, driven by a dynamic blocking synoptic pattern (omega block) creating conditions for a powerful northeastward circulation of air masses rich in dust and moisture. Here, we study and characterize the effects of related dust intrusion over Sofia, Bulgaria, using lidar remote sensing combined with in situ measurements, satellite imagery, and modeling data. Optical and microphysical parameters of the desert aerosols were obtained and vertically profiled, namely, backscatter coefficients and backscatter-related Ångström exponents, as well as statistical distributions of the latter as qualitative analogs of the actual particle size distributions. Dynamical and topological features of the dust-dominated aerosol layers were determined. Height profiles of the aerosol/dust mass concentration were obtained by synergistic combining and calibrating lidar and in situ data. The comparison of the retrieved mass concentration profiles with the dust modeling ones shows a satisfactory compliance. The local meteorological conditions and the aerosol composition and structure of the troposphere above Sofia during the dust event were seriously affected by the desert air masses.

STUDY OF SINGLE- AND DOUBLE-MOMENT MICROPHYSICS SCHEME IMPACT ON LILI AND MANGGA TROPICAL CYCLONE

<em>In this study, prediction of tropical cyclones using the Weather Research and Forecasting (WRF) model was used to test the double-moment (DM) and single-moment (SM) microphysical parameterization schemes in event of Lili and Mangga Tropical Cyclones. Models with microphysical parameterization schemes WDM5, WDM6, WSM5, WSM6, and without microphysical parameterization schemes (CTL) were each tested against track predictions, the pressure value, and maximum wind speed. The results of track prediction show that the best schemes in the tropical cyclone case of Lili and Mangga is WSM6 and WDM6, respectively, with an average error value of 78.1 and 80.1 km. Based on the Taylor diagram, the prediction results of the pressure value and the maximum wind speed in case of Lili Tropical Cyclones get the WDM6 scheme as the best scheme. Meanwhile, the results of the pressure prediction at the cyclone center in the case of Mangga Tropical Cyclones show that the WDM6 scheme is the best. However, the prediction of maximum wind speed in Mangga tropical cyclones produces the CTL scheme as the best scheme. This study shows that DM dan SM microphysical parameterization schemes have a big impact on track prediction compare to pressure value and maximum wind speed variable.</em>