Atmos. Chem. Phys., 16, 11367–11378, 2016www.atmos-chem-phys.net/16/11367/2016/doi:10.5194/acp-16-11367-2016© Author(s) 2016. CC Attribution 3.0 License.

Cloud ice caused by atmospheric mineral dust – Part 1:Parameterization of ice nuclei concentration in theNMME-DREAM modelSlobodan Nickovic1, Bojan Cvetkovic1, Fabio Madonna2, Marco Rosoldi2, Goran Pejanovic1, Slavko Petkovic1, andJugoslav Nikolic1

1Republic Hydrometeorological Service of Serbia, 11000 Belgrade, Serbia2Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi Ambientale, 85050, Tito Scalo, Potenza, Italy

Correspondence to: Slobodan Nickovic (nickovic@gmail.com)

Received: 12 May 2016 – Published in Atmos. Chem. Phys. Discuss.: 18 May 2016Revised: 25 August 2016 – Accepted: 26 August 2016 – Published: 14 September 2016

Abstract. Dust aerosols are very efficient ice nuclei, impor-tant for heterogeneous cloud glaciation even in regions dis-tant from desert sources. A new generation of ice nucleationparameterizations, including dust as an ice nucleation agent,opens the way towards a more accurate treatment of coldcloud formation in atmospheric models. Using such param-eterizations, we have developed a regional dust-atmosphericmodelling system capable of predicting, in real time, dust-induced ice nucleation. We executed the model with theadded ice nucleation component over the Mediterranean re-gion, exposed to moderate Saharan dust transport, over twoperiods lasting 15 and 9 days, respectively. The model resultswere compared against satellite and ground-based cloud-ice-related measurements, provided by SEVIRI (Spinning En-hanced Visible and InfraRed Imager) and the CNR-IMAAAtmospheric Observatory (CIAO) in Potenza, southern Italy.The predicted ice nuclei concentration showed a reasonablelevel of agreement when compared against the observed spa-tial and temporal patterns of cloud ice water. The developedmethodology permits the use of ice nuclei as input into thecloud microphysics schemes of atmospheric models, assum-ing that this approach could improve the predictions of cloudformation and associated precipitation.

1 Introduction

Aerosols acting as ice-nucleating particles enhance the het-erogeneous glaciation of cloud water, making it freeze earlierand at higher temperatures than otherwise. Insoluble parti-cles, such as dust and biological particles, are known as thebest ice nuclei. Cziczo et al. (2013), hereinafter referred toas CZ13, show that mineral dust and metallic oxide particles,found as residues in the ice crystals of aircraft measurementsover North and Central America, are prevailing (61 %). Con-cerning other aerosol types, CZ13 show that, in the regionsdistant from dust sources, sea salt represents only 3 % in theregions away from the open ocean, whereas elemental carbonand biological particles represent less than 1 %. Furthermore,CZ13 demonstrate that the dominant ice nucleation (IN) is aheterogeneous immersion process in 94 % of the collectedsamples. During IN, only a small number of dust particles,a few in a standard litre, are sufficient to trigger the cloudglaciation process at temperatures lower than −20 ◦C (De-Mott et al., 2015). Since dust in small concentrations is eas-ily lifted to the mid- and upper troposphere, the cold cloudsformed due to dust can be found at locations distant fromdust deserts (Creamean et al., 2015; CZ13).

Mineral dust particles as significant contributors in INare associated with the cloud formation and precipitation(Sassen, 2005; DeMott et al., 2003; Yakobi-Hancock et al.,2013). For example, the measurement of ice residues fromin situ cold cloud samples and the precipitation measure-ments collected in California strongly suggest that the non-soluble aerosol originating from the Asian and Saharan dust

Published by Copernicus Publications on behalf of the European Geosciences Union.

11368 S. Nickovic et al.: Cloud ice caused by atmospheric mineral dust – Part 1

sources (dust and biological aerosol) enhances both ice for-mation in mid-level clouds and precipitation (Ault et al.,2011; Creamean et al., 2015). Recent modelling experimentsconfirm that in the pristine environment dust and biologicalaerosols could increase the precipitation as well (Fan et al.,2014). In this process, there is little influence of dust chemi-cal aging (DeMott et al., 2015). Another study (Rosenfeld etal., 2011) indicates that desert dust has the ability to glaciatethe top of developing convective clouds, creating ice precipi-tation instead of suppressing warm rain; the dust invigorationeffect would also enhance precipitation. On the other hand,Teller et al. (2012) conclude from their modelling study thatthe presence of mineral dust had a much smaller effect onthe total precipitation than on its spatial distribution, whichindicates that quantification of dust effects to precipitation isstill uncertain because dust could modify cloud properties inmany complex ways (Huang et al., 2014); therefore, impactsof dust on cloud processes require further research.

The large interest in ice nucleation research, illustrated bythe exponential growth of published articles in this field (De-Mott et al., 2011) has been motivated, inter alia, by the needsof the community to improve the unsatisfactory representa-tion of cloud formation in atmospheric models and there-fore to increase the accuracy of weather and climate predic-tions. Older parameterizations (Fletcher, 1962; Meyers et al.,1992) considered the concentration of ice nuclei concentra-tion (nIN) only as a function of the temperature and ice sat-uration ratio. However, more recent observations show that,at a given temperature and moisture, nIN depends on aerosolconcentration as well. Based on this evidence, a new gener-ation of nIN parameterizations has been developed (DeMottet al., 2010, 2015; Niemand et al., 2012; Tobo et al., 2013;Phillips et al., 2013; Atkinson et al., 2013), where dust is rec-ognized as one of the major nIN input parameters, in whichnIN represents the fraction of dust aerosol capable of produc-ing cloud water ice.

Exploiting these findings, we have developed a coupledregional real-time forecasting system (composed of theatmospheric NMME model and the DREAM dust model),which predicts dust-caused nIN as an online model variable.When parameterizing nIN, the immersion and depositionmodes of freezing have been assumed to drive the iceformation process. Such a new parameter will be used inour future study as an input to a microphysics scheme, withthe expectation to improve the operational prediction ofcold clouds and associated precipitation. Currently, nIN isnot used as an online prognostic variable in either of theoperational dust models of two largest international dustforecasting networks: in the WMO Sand and Dust Warningand Assessment System (SDS-WAS) (http://www.wmo.int/pages/prog/arep/wwrp/new/Sand_and_Dust_Storm.html)and in the ICAP Multi-Model Ensemble (ICAP-MME)(http://icap.atmos.und.edu/). Unlike dust models of thesenetworks, our modelling system predicts nIN at every modeltime step.

The model description and the implemented nIN param-eterizations are presented in Sect. 2. The observations usedfor the model evaluation and the model performance are pre-sented in Sect. 3. The comparisons of model simulationsagainst observations are described in Sect. 4. Conclusions aregiven in Sect. 5.

2 Modelling

The evidence on the dominant role of dust in cold cloud for-mation has motivated a number of research groups to linkcloud microphysics schemes with the parameterizations ofdust-affected nIN in atmospheric models. The atmosphericmodels which drive ice nucleation parameterizations rangefrom simplified 1-D and 1.5-D kinematic or trajectory mod-els (Field et al., 2012; Eidhammer et al., 2010; Dearden et al.,2012; Simmel et al., 2015) to complex full atmospheric mod-els (e.g. Niemand et al., 2012; Thompson and Eidhammer,2014). However, only a few such models (used in weatherand/or climate applications) have dust concentration as aforecasting parameter available for online nIN calculation.For example, in a dust event study, Niemand et al. (2012)used the temperature and dust particle surface area predictedby the regional-scale online coupled model COSMOART(Consortium for Small-Scale Modelling–Aerosols and Re-active Trace Gases) to calculate immersion freezing nIN.The model has been compared against observations usingthe chamber-processed nIN calculated from the ground-basedaerosol concentration measurements, but not against directlyobserved cloud ice. Furthermore, Hande et al. (2015) haveimplemented the COSMO model coupled to the MUlti-ScaleChemistry Aerosol Transport (MUSCAT) model to computea seasonal variability of nIN. However, this model has notbeen compared against daily observations. The model whichgets close to the real-time forecasting of glaciated clouds isa “dust-friendly” version of the bulk microphysics scheme(Thompson and Eidhammer, 2014), with explicitly incorpo-rated dust aerosols. However, this model currently uses a cli-matological rather than predicted dust concentration for nINcalculations.

Following the objective of this study to develop a methodfor real-time nIN prediction, we have used the Dust RegionalAtmospheric Model (DREAM) driven by the National Cen-ters for Environmental Predictions (NCEP) nonhydrostaticmultiscale atmospheric model on the E grid (North Amer-ican Multi-Model Ensemble, NMME; Kirtman et al., 2014),in which we have incorporated a parameterization of the icenuclei concentration calculated at every model time step as afunction of dust concentration and atmospheric variables.

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2.1 NMME model

NMME (Janjic et al., 2001, 2011; Janjic, 2003) has beenused for various applications at NCEP and elsewhere sincethe early 2000s. Since 2006 it has been the main operationalshort-range weather forecasting North American model. Itis also used for operational regional forecasts in the Repub-lic Hydrometeorological Service of Serbia. The NMME dy-namics core includes energy/enstrophy horizontal advection,vertical advection, a nonhydrostatic add-on module, lateraldiffusion, horizontal divergence damping, sub-grid gravitywaves, and transport of moisture and different passive trac-ers. Concerning the model physics, there are various optionalmodules: cloud microphysical schemes ranging from simpli-fied ones suitable for mesoscale modelling to sophisticatedmixed-phase physics for cloud resolving models; cumulusparameterizations; surface physics; planetary boundary layerand free-atmosphere turbulence; and atmospheric longwaveand shortwave radiation schemes. NMME uses a hybrid ver-tical coordinate with a terrain-following sigma in the loweratmosphere and a pressure coordinate in the upper atmo-sphere.

2.2 DREAM model

DREAM (Nickovic et al., 2001; Nickovic, 2005; Pejanovicet al., 2011) was developed to predict the atmospheric dustprocess, including dust emission, dust horizontal and verti-cal turbulent mixing, long-range transport and dust deposi-tion. Eight radii bins in the model range from 0.15 to 7.1 µm.Dust emission parameterization includes a viscous sub-layerbetween the surface and the lowest model layer (Janjic, 1994)in order to parameterize the turbulent vertical transfer ofdust into the lowest model layer following different turbulentregimes (laminar, transient and turbulent mixing). The wetdust removal is proportional to the rainfall rate. The speci-fication of dust sources is based on the mapping of the ar-eas that are dust-productive under favourable weather condi-tions. The USGS land cover data combined with the prefer-ential dust sources of dust originating from the sediments inpalaeo-lake and riverine beds (Ginoux et al., 2001) have beenused to define barren and arid soils as dust-productive areas.

2.3 Ice nucleation parameterization

In this study, dust concentration, atmospheric temperatureand moisture as predicted by the atmospheric component ofthe coupled model were used for nIN calculation. The nIN pa-rameterization consists of two parts, applied to warmer andcolder glaciated clouds.

For temperatures in the interval (−36 ◦C;−5 ◦C), we haveimplemented the immersion ice nucleation parameterizationdeveloped by DeMott et al. (2015):

nIN = C(ndust)(α(273.16−T )+β)exp(γ (273.16−T )+δ), (1)

where nIN is the number concentration of ice nuclei (L−1),ndust is the number concentration of dust particles with a di-ameter larger than 0.5 µm (cm−3), T is the temperature inCelsius degrees, α = 0, β = 1.25, γ = 0.46, and d =−11.6.Equation (1) is applied when relative humidity with respectto ice is exceeding 100 %. This parameterization scheme hasbeen developed as an extension of DeMott et al. (2010) andTobo et al. (2013), but applied exclusively to mineral dustnIN collected in laboratory and field measurements. With theDeMott et al. (2010) approach, the spread of errors in pre-dicting IN concentrations at a given temperature has beenreduced from a factor of ∼ 1000 to ∼ 10. Their parameter-ization is based on the use of observations from a numberof field experiments at a variety of geographic locations overa period longer than a decade, demonstrating that there is acorrelation between the observed nIN and the dust numberconcentrations of particles larger than 0.25 µm in radius. InDeMott et al. (2015), C= 3 is chosen as a calibration factorto adjust the scheme to dust measurements. Despite the factthat validity of the scheme is for temperatures colder than−20 ◦C we extrapolated its application down to −5 ◦C in or-der to test whether the model can predict the occurrence oflower mixed clouds for temperatures in the interval (−20◦ C;−5◦ C).

For temperatures in the interval (−55 ◦C; −36 ◦C), wehave implemented the Steinke et al. (2015) parameterizationfor the deposition ice nucleation based on the ice nucleationactive surface site approach in which nIN is a function oftemperature, humidity and the aerosol surface area concen-tration. In the deposition nucleation, water vapour is directlytransformed into ice at the particle’s surface, at the time ofor shortly after the water condensation on the particle, whichacts at the same time as a condensation and freezing nucleus.Steinke et al. (2015) calculate the number concentration ofice nuclei due to deposition freezing as

nIN = pSdustexp[−q (T − 273.16)+ (rRHice− 100)], (2)

where nIN is the number concentration of ice nuclei (cm−3),Sdust is the ice-active surface site density (m−2) (Niemand etal., 2012) describing the efficiency of a dust particle to freezethe cloud water, p = 188× 105, q =−1.0815, r =−0.815,T is temperature in degrees Celsius, and RHice is relative hu-midity with respect to ice. In our experiments, RHice is pre-specified to the value of 110 %.

Although based on two different parameterizations, the re-sulting nIN has a smooth transition across the temperatureboundary of −36 ◦C between the DeMott et al. (2015) andSteinke et al. (2015) schemes. At this transitional tempera-ture, we have not applied any mathematical smoothing.

The schemes of DeMott et al. (2015) and Steinke etal. (2015) require temperature, relative humidity and dustconcentration as input parameters, but not vertical velocityas used in some other microphysical schemes (e.g. Wang etal., 2014).

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3 Observations

The capabilities of the model to predict vertical features ofdust and cold clouds have been evaluated using vertical pro-files of the aerosol and cloud properties routinely measuredat the CNR-IMAA Atmospheric Observatory (CIAO) at TitoScalo (Potenza), Italy, using several ground-based remotesensing techniques, such as lidar, radar and passive tech-niques. MUSA (Multiwavelength System for Aerosol) is amobile multi-wavelength lidar system based on an Nd:YAGlaser equipped with second- and third-harmonic generatorsand on a Cassegrain telescope with a primary mirror of300 mm diameter. The three laser beams at 1064, 532 and355 nm are simultaneously and coaxially transmitted into theatmosphere in biaxial configuration. The receiving systemhas three channels for the detection of the radiation elasti-cally backscattered from the atmosphere and two channelsfor the detection of the Raman radiation backscattered by theatmospheric N2 molecules at 607 and 387 nm. The elasticchannel at 532 nm is split into parallel and perpendicular po-larization components by means of a polarizer beam-splittercube. The calibration of depolarization channels is made au-tomatically using the ±45 method. The typical vertical res-olution of the raw profiles is 3.75 m with a temporal resolu-tion of 1 min. It is worth emphasizing that multi-wavelengthRaman lidar measurements allow the user to not only mon-itor the dynamical evolution of aerosol particles in the tro-posphere but also identify the different aerosol types (Bur-ton et al., 2013; Groß et al., 2015), taking advantage of thelarge number of optical properties they are able to provide,i.e. lidar ratio at two wavelengths, the Ångström exponent,the backscatter-related Ångström exponent, and linear parti-cle depolarization ratio. This aerosol typing capability allowsthe user to classify the aerosol type acting as nIN, and espe-cially to separate mineral dust from other types of aerosol.

CIAO, as one of the Cloudnet stations (http://www.cloud-net.org), applies the Cloudnet retrieval scheme to pro-vide vertical profiles of cloud types. Cloudnet processing isbased on the use of ceilometer, microwave radiometer andcloud radar observations. For the CIAO station (Madonnaet al., 2010, 2011), the Cloudnet processing involves obser-vations provided by a Vaisala CT25k ceilometer, a Radio-metrics MP3014 microwave profiler, and a Metek MIRA36millimetre-wavelength Doppler polarimetric cloud radar. Inparticular, MIRA36 It is a monostatic magnetron-basedpulsed Ka-band Doppler radar for unattended long-term ob-servation of clouds properties. In the configuration operativeat CIAO, a linear polarized signal is transmitted while co-and cross-polarized signals are received simultaneously todetect Doppler spectra of the reflectivity and linear depolar-ization ratio (LDR). The reflectivity is used to determine thedensity of cloud constituents, while LDR helps to identifythe target type. The radar has a 1 m diameter antenna andemits microwave radiation at 35.5 GHz with a peak power of30 kW, a pulse width of 200 ns and a pulse repetition rate of

5 kHz. The antenna beam width is 0.6◦× 0.6◦ (gain 49 dBi)and the radar sensitivity is−40.3 dBZ at 5 km (0.1 s time res-olution), while the Doppler velocity resolution is 0.02 m s−1.The LDR accuracy is within ±2.0 dB. The receiver calibra-tion is within an accuracy of less than ±1 dB. This system isable to provide highly accurate measurements of the reflec-tivity factor with a vertical resolution up to 15 m, though thecurrent configuration is set to a vertical resolution of 30 m.The radar is a 3-D scanning system, but Cloudnet processingmakes use of zenith-pointing observations only.

Cloudnet processing provides the categorization of the ob-served vertical profiles of cloud water categories, such asliquid droplets, ice particles, aerosols and insects. This cat-egorization is essentially based on different sensitivities ofthe lidar and radar to different particle size ranges. For layersidentified as ice clouds, the ice water content (with the relateduncertainty) is derived from radar reflectivity factor and airtemperature using an empirical formula based on dedicatedaircraft measurements (Hogan et al., 2005). Consistency be-tween Cloudnet products and Raman lidar observations ofclouds performed at CIAO has also been examined (Rosoldiet al., 2016).

To complement the Potenza in situ profiling observationsand to examine how the model predicts horizontal distribu-tion of cold clouds, the MSG-SEVIRI ice water path satelliteobservations were used. SEVIRI (Spinning Enhanced Visi-ble and InfraRed Imager), as a geostationary passive imager,is on board the Meteosat Second Generation (MSG) sys-tems. The high SEVIRI spatial and temporal resolution (∼ 4and 15 min, respectively), among other advantages, provideshigh-quality products. The inputs to the retrieval schemeswere inter-calibrated effective radiances of Meteosat-8 and9. In our study, the daily averages of the retrieved ice waterpath of the SEVIRI cloud property dataset (CLAAS) wereused (Stengel et al., 2013, 2014) to compare the model re-sults against their observations:

IWP=23rIreffτ.

Here, IWP [g m−2] is the ice water path, τ is the verticallyintegrated cloud optical thickness at 0.6 µm derived in satel-lite pixels assigned to be cloud-filled, reff is the surface-area-weighted radius of cloud particles (µm), rI = 0.93 g cm−3 isthe ice water density.

4 Model experiments and validation

The model domain covers northern Africa, southern Europeand the Mediterranean. The model resolution has been setto 25 km in the horizontal and to 28 layers in the vertical,ranging from the surface to 100 hPa. At the horizontal modelresolution (which relates to the hydrostatic type of thermo-dynamics), clouds are resolved by the following schemes:the parameterization of grid-scale clouds and microphysics

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S. Nickovic et al.: Cloud ice caused by atmospheric mineral dust – Part 1 11371

Figure 1.

(Ferrier et al., 2002); and the parameterization of convec-tion clouds (Janjic, 1994, 2000). The initial and boundaryatmospheric conditions for the NMME model have been up-dated every 24 h using the ECMWF 0.5◦ analysis data. Theconcentration was set to zero at the “cold start” of DREAMlaunched 4 days before the period to be studied, thus permit-ting the model to be “warmed up”, i.e. to develop a meaning-ful concentration field at the date considered as an effectivemodel start. After that point of time, 24 h dust concentration

forecasts from the previous-day runs have been declared asinitial states for the next-day run of DREAM.

The coupled NMME-DREAM model has been run andcompared against ground-based and satellite observations fortwo periods (1–15 May 2010 and 20–29 September 2012)during which the CIAO Potenza instruments observed anoccasional occurrence of Saharan dust accompanied witha sporadic formation of mixed-phase and/or cold clouds.These periods, characterized by modest rather than majordust transport into the Mediterranean, have been intention-

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11372 S. Nickovic et al.: Cloud ice caused by atmospheric mineral dust – Part 1

Figure 1. Daily averages of (a) the model dust load (g m−2), (b) the model NL, (c) the MSG-SEVIRI IWPL, and (d) overlap of normalizedNL and IWPL. Colour selection: hits – blue; misses – green; false alarm – brown.

ally chosen to find out whether non-intensive dust conditionscan still form cold clouds.

For the May 2010 period, a detailed day-by-day compar-ison of the model against SEVIRI data is shown in Fig. 1.It is important to mention that, during the periods 8–9 and13–14 May, the Eyjafjallajökull volcanic cloud was also ob-

served in Potenza (Mona et al., 2012; Pappalardo et al.,2013), thus potentially interfering with dust. The possible in-fluence of the existing volcanic ash on our results is discussedlater in the text.

Figure 1 shows the mapped daily averages of the follow-ing variables: the model vertical dust load (DL), the model

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Figure 2. Time evolution of the forecast accuracy (black), the prob-ability of detection (hit rate, red) and the false alarm ratio (green)for the period 1–15 May 2010.

NL= log10∫nINdz, the MSG-SEVIRI IWPL= log10(IWP),

and the overlap of NL and IWPL; columns in the figureshowing these variables are marked by a, b, c, and d, respec-tively. From columns a and b in Fig. 1 one can observe ageneral lack of agreement between DL and NL. This differ-ence is expected since the cold cloud formation is dependentnot only on dust but also on its complex interaction with theatmospheric thermodynamic conditions. On the other hand,a visual inspection shows a considerable similarity betweenNL and the IWPL patterns (columns b and c) with respect totheir shapes and locations.

The maps in column d show how much the normalized NLand IWPL daily averages are overlapping. Hits, misses andfalse alarms are represented by areas shaded in blue, greenand brown, respectively. One can notice that the overlap-ping (hits) always represents the largest parts of the showndaily maps. Although not dominant, there are certain regionsof cold clouds either observed but not predicted (misses) orpredicted but not observed (false alarms). The former caseshould not necessarily be erroneous because it might be ad-dressing the processes not represented by our parameteriza-tion: the clouds generated by homogeneous glaciation or theclouds made by heterogeneous freezing with aerosols otherthan dust.

To gain additional evidence on the matching between NLand IWPL, we used their normalized daily averages to cal-culate the following statistical dichotomous (yes/no) scoresbased on hits, misses and correct negatives (not predicted,not observed) (WMO, 2009):

– accuracy – showing what fraction of the forecasts werecorrect;

– probability of detection (hit rate) – showing what frac-tion of the observed “yes” events were correctly fore-casted;

– the false alarm ratio – showing what fraction of the pre-dicted “yes” events actually did not occur.

The scores were calculated using the values for all modelgrid points and observations and for all days of the consid-ered period. Figure 2 shows the time evolution of the scores(which by definition range between 0 and 1). In average forthe whole period, 63.4 % of all NL were correct with respectto IWPL, 73.9 % of the observed IWPL were predicted, andfor 30.4 % of the forecast NL, IWPL was not observed. Suchresult confirms a high level of matching between two fieldsshown in Fig. 1.

Additional evidence on matching between our forecastsand satellite observations has been made by applying theMethod for Object-based Diagnostic Evaluation (MODE;Davis et al., 2006a, b, 2009), which is based on a fuzzy-logicalgorithm and which was originally developed to quantifythe errors related to spatial patterns and location of precipi-tation considering various attributes of rain patterns (e.g. ori-entation, rain area). Factors such as the separation of the ob-ject (pattern) centroids, minimum edge separation betweenmodelled and observed patterns, modelled or observed pat-terns’ orientation angles relative to the grid axis, the ratio ofthe areas of the two objects, and the fraction of area com-mon to both objects. MODE is used here to indicate thelevel of matching between NL and IWPL for the selectedday of 11 May 2010. Figure 3 shows that MODE has identi-fied three precipitation objects: two (green and red) showinggood matching and one (blue) with no matching.

To evaluate the model performance in representing thevertical structure of the ice water clouds, nIN was com-pared with the observed ice water content (IWC) obtainedusing the Cloudnet retrieval scheme over Potenza. Figure 4shows time evaluation of log10(nIN) (colour-shaded) andlog10(IWC× 10−6 kg m3), (contour-plotted) over periods 1–15 May 2010 and 22–30 September 2012. In addition, thered contours show the temperature field as provided bythe NMME model. The different quantities provided byDREAM and Cloudnet to characterize the cloud ice content.Note that IWC nIN are different physical variables and there-fore a semi-quantitative comparison is possible.

The comparisons reveal general good performances ofDREAM in predicting the vertical structure of the observedice clouds for temperatures below −20 ◦C, which coincideswith the validity range of DeMott et al. (2015). Especiallyduring 1–15 May 2010, a remarkable agreement betweenpatterns of the ice vertical layer retrieved using the cloudradar observation and the nIN predicted by the model isevident. However, most of the ice nuclei concentration fortemperatures warmer than −20 ◦C was not predicted, al-though mixed clouds were observed by the cloud radar below4 km a.g.l. This is particularly evident on 6 May, when icecloud layer below 3 km a.g.l. was observed only by the radarand conditions for ice occurrence were completely missed bythe model.

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11374 S. Nickovic et al.: Cloud ice caused by atmospheric mineral dust – Part 1

−20 −10 0 10 20 30

1520

2530

3540

45

Satellitefeature field

−20 −10 0 10 20 30

1520

2530

3540

45

Modelfeature field

0

1

2

3

4

IWPsmooth= 5 tresh= 0.3 0.6 (non)

Figure 3. Best matching pairs of entities identified using the MODE method: red and green identify objects that are matched, while darkblue and grey indicates no matching; the x and y axes are longitudes and latitudes, respectively.

Figure 4. Comparison of log10(IWC× 10−6 kg m3) obtained from the Doppler radar reflectivity using the Cloudnet algorithm (solid blackline contour plot) vs. DREAM log10(nIN) (coloured shaded plot) for the periods 1–15 May 2010 (left) and 22–30 September 2012 (right).Red contours show temperature as provided by the NMME model.

On 22–30 September 2012 the model was able to indicateof the deep ice layers observed on 25–27 September 2012between about 5 and 12 km a.g.l. (−10 and −60 ◦C), and itwas able to partially predict a part of the thinner layers ob-served after 27 September above 7 km a.g.l. (<−25 ◦C). Themodel was also able to predict well the occurrence of cirrusclouds observed by the cloud radar on 29 September in therange between 6 and 12 km. It is also worth mentioning thatthe co-located and simultaneous Raman lidar measurements(not reported) showed some high optically thin cloudinessnot detected by the radar because of its limited sensitivityto thin clouds at that height (Borg et al., 2011). In particular,the nIN layers predicted by the model in the second half of 27and on 28 September are in the range between 9 and 12 km.However, as in the case of May 2010, the model underpre-dicted nIN for the lowest ice water layers observed with theradar below 4 km.

The inability of the model to predict nIN at lower el-evations can be explained by the fact that the DeMott et

al. (2015) parameterization is valid for temperatures in theinterval (−20 to −36 ◦C). We extended this scheme to workin the interval (−5 ◦C; −20 ◦C) as well, but our experimentsshowed that lower mixed clouds could not be predicted. Thisresult is consistent with the statement of DeMott et al. (2015)that the parameterization is weakly constrained at temper-atures warmer than −20◦. As these authors also claimed,this is the temperature regime that may be dominated by or-ganic ice-nucleating particles such as ice-nucleating bacteria,which is an aerosol not included in our parameterizations.

In Fig. 5 we also report the comparison of IWPL and NLover Potenza calculated every 3 h, in the period from 1 to15 May 2010 (left panel) and from 22 to 30 September 2012(right panel). The outcome of the comparison confirms thegood performance of the model in the prediction of nIN ofthe ice clouds over the whole atmospheric column.

The correlation between the IWPL and NL retrieved us-ing the ground-based measurements, merging the datasetsfrom both selected cases studies of 1–15 May 2010 and 22–

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Figure 5. Time evolution of IWPL and NL over the periods 1–15 May 2010 (left) and 22–30 September 2012 (right).

30 September 2012, is shown in Fig. 6. The linear corre-lation made considering the daily averages for both quanti-ties provides a regression coefficient of R = 0.83. The scat-ter plot shows a large variability in the values correspond-ing to the higher values of the IWP and to the higher val-ues of IL. Therefore, for optically thinner ice clouds, IL lin-early increases with IWPL. For larger IWPL values, the twovariables are less correlated and the second- or higher-orderpolynomial fitting could compromise the linear relationship.In the period between 13 and 15 May 2010, both DREAMand back-trajectory analysis showed that, while the trans-port of volcanic aerosol from Iceland (due to the eruptionof Eyjafjallajökull in 2010) was still ongoing, dust contribu-tion was not negligible (Mona et al., 2012). In this period,the volcanic aerosol was mainly transported across the At-lantic Ocean, passing over Ireland and the western UK, andthen transported to the west of the Iberian Peninsula beforereaching the Mediterranean Basin and southern Italy. Satel-lite images and ground-based measurements confirmed thepresence of volcanic particles in the corresponding regions(not shown). A detailed description of the volcanic layersas observed by EARLINET (European Aerosol Research Li-dar NETwork) during this period is reported in Pappalardoet al. (2014). The EARLINET volcanic dataset is freelyavailable at http://www.earlinet.org (the EARLINET pub-lishing group 2000–2010, 2014: EARLINET observationsrelated to volcanic eruptions (2000–2010); World Data Cen-ter for Climate (WDCC), http://dx.doi.org/10.1594/WDCC/EN_VolcanicEruption_2000-2010). Moreover, a devoted re-lational database freely available at http://www.earlinet.orgcontains all information about volcanic layers (base, top, cen-tre of mass) and corresponding mean and integrated values.

The Iberian Peninsula, France and southern Italy were theregions more significantly affected by the presence of vol-canic aerosol (sulfate and small ash) during the consideredperiod. For the purpose of our modelling study this mightinduce an underestimation of the IN (since IN due to dustonly is modelled) in the above-mentioned regions and can beresponsible for part of the discrepancies between modelled

Figure 6. Linear correlation between IWPL and NL retrieved us-ing the ground-based measurements merging the datasets from bothselected case studies: 1–15 May 2010 and 22–30 September 2012.

IN and IWP provided by SEVIRI. This is particularly truefor Iberian Peninsula, where volcanic aerosol concentrationswere quite relevant. The comparison of model-predicted INand SEVIRI IWC on 13 May shows differences that mightbe correlated with a larger availability of IN of volcanic ori-gin. However, in southern Italy, the volcanic layer, observedat Potenza up to an altitude up to 15.8 km above sea level, didnot enhance the formation of cold clouds due to unfavourabledry conditions in the free troposphere; this is also confirmedby the Potenza cloud radar, which did not observe clouds forthe whole day (Fig. 4). The absence of cold clouds over mostof southern Italy, including the Potenza region, is also shownby the IWC reported for 13 May in Fig. 1.

5 Conclusions

We have expanded the regional DREAM-NMME modellingsystem with the online parameterization of heterogeneousice nucleation caused by mineral dust aerosol. We employedthe recently developed empirical parameterizations for im-

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11376 S. Nickovic et al.: Cloud ice caused by atmospheric mineral dust – Part 1

mersion and deposition ice nucleation that include dust con-centration as a dependent variable for the cloud glacia-tion process. In our approach, the ice nucleation concen-tration was calculated as a prognostic parameter depend-ing on dust and atmospheric thermodynamic conditions. Toour knowledge, this is one of first attempts to predict inreal time all components needed for parameterization ofdust-induced cold cloud formation within one modellingsystem. Experimental NMME-DREAM nIN daily predic-tions compared against SEVIRI observations are posted athttp://dream.ipb.ac.rs/ice_nucleation_forecast.html to showoperational capabilities of the methodology presented in thisstudy.

The model was applied for the Mediterranean region andsurroundings for two periods (1–15 May 2010 and 22–30 September 2012) during which several dust transportevents of moderate intensity occurred. The model has beencompared against both ground-based and satellite observa-tions for two periods, with the aim of checking the perfor-mance over both the horizontal and vertical cross sectionsof the investigated atmosphere, providing promising results.Somewhat lower performance of the model in representingice layers at lower altitudes could have been affected by thecapability of the parameterization scheme to predict mixed-phase clouds in the zone of warmer negative temperatures.

Our study aimed to develop a methodology which lays thegroundwork for further improvement of predicting cloudsand associated precipitation in current atmospheric mod-els. Operational numerical weather prediction systems todayusually do not include aerosol effects in cloud formation orthey do it in a simplistic way. By integrating dust and atmo-spheric components into a modelling system, we managedto have all necessary components at every time step – atmo-spheric and aerosol parameters – to calculate the ice nucleiconcentration formed by dust, which will be used in our fu-ture development as an input into a dust-friendly cloud mi-crophysics to predict the ice mixing ratio.

6 Data availability

The measurement data are available upon request from coau-thor Fabio Madonna (fabio.madonna@imaa.cnr.it). Datafrom the model simulations are available from the corre-sponding author on request.

Acknowledgements. We acknowledge EUMETSAT for use ofits Satellite Application Facility on Climate Monitoring (CMSAF) data. We are grateful to Beth Ebert (Bureau of MeteorologyResearch Centre, Melbourne, Australia) for guidance and discus-sions on object-oriented model validations. We thank Luka Ilic(Institute of Physics Belgrade, Serbia) and Milica Arsic (RepublicHydrometeorological Service of Serbia) for their technical assis-tance. Support for the modelling research was provided by theRepublic Hydrometeorological Service of Serbia. Support for the

observational research provided by CNR-IMAA was funded bythe following projects: EARLINET under EU grant RICA 025991as part of the Sixth Framework Programme; the Cloudnet project(EU contract EVK2-2000-00611) for the ice water content, whichwas produced by CNR-IMAA using measurements from Potenza;and ACTRIS through the EU’s Seventh Framework Programme(FP7/2007-2013) under grant 262254 (including ACTRIS TNA).

Edited by: J. HuangReviewed by: two anonymous referees

References

Atkinson, J. D., Murray, B. J., Woodhouse, M. T., Whale, T. F.,Baustian, K. J., Carslaw, K. S., Dobbie, S., O’Sullivan, D., andMalkin, T. L.: The importance of feldspar for ice nucleationbymineral dust in mixed-phase clouds, Nature, 498, 355–358,2013.

Ault, A. P., Williams, C. R., White, A. B., Neiman, P. J., Creamean,J. M., Gaston, C. J., Ralph, F. M., and Prather, K. A.: Detectionof Asian dust in California orographic precipitation, J. Geophys.Res.-Atmos., 116, D16205, doi:10.1029/2010JD015351, 2011.

Borg, L. A., Holz, R. E., and Turner, D. D.: Investigatingcloud radar sensitivity to optically thin cirrus using collocatedRaman lidar observations, Geophys. Res. Lett., 38, L05807,doi:10.1029/2010GL046365, 2011.

Burton, S. P., Ferrare, R. A., Vaughan, M. A., Omar, A. H.,Rogers, R. R., Hostetler, C. A., and Hair, J. W.: Aerosolclassification from airborne HSRL and comparisons with theCALIPSO vertical feature mask, Atmos. Meas. Tech., 6, 1397–1412, doi:10.5194/amt-6-1397-2013, 2013.

Creamean, J. M., Ault, A. P., White, A. B., Neiman, P. J., Ralph, F.M., Minnis, P., and Prather, K. A.: Impact of interannual varia-tions in sources of insoluble aerosol species on orographic pre-cipitation over California’s central Sierra Nevada, Atmos. Chem.Phys., 15, 6535–6548, doi:10.5194/acp-15-6535-2015, 2015.

Cziczo, D. J., Froyd, K. D., Hoose, C., Jensen, E. J., Diao, M.,Zondlo, M. A., Smith, J. B., Twohy, C. H., and Murphy, D.M.: Clarifying the Dominant Sources and Mechanisms of CirrusCloud Formation, Science, 340, 1320–1324, 2013.

Davis, C. A., Brown, B., and Bullock, R.: Object-Based Verificationof Precipitation Forecasts. Part I: Methodology and Applicationto Mesoscale Rain Areas, Mon. Weather Rev., 134, 1772–1784,doi:10.1175/MWR3145.1, 2006a.

Davis, C. A., Brown, B., and Bullock, R.: Object-Based Verifi-cation of Precipitation Forecasts. Part II: Application to Con-vective Rain Systems, Mon. Weather Rev., 134, 1785–1795,doi:10.1175/MWR3146.1, 2006b.

Davis, C. A., Brown, B. G., Bullock, R., and Halley-Gotway,J.: The Method for Object-Based Diagnostic Evaluation(MODE) Applied to Numerical Forecasts from the 2005NSSL/SPC Spring Program, Weather Forecast., 24, 1252–1267,doi:10.1175/2009WAF2222241.1, 2009.

Dearden, C., Connolly, P. J., Choularton, T., Field, P. R., andHeymsfield, A. J.: Factors influencing ice formation and growthin simulations of a mixed-phase wave cloud, J. Adv. Model.Earth Syst., 4, M10001, doi:10.1029/2012MS000163, 2012.

Atmos. Chem. Phys., 16, 11367–11378, 2016 www.atmos-chem-phys.net/16/11367/2016/

S. Nickovic et al.: Cloud ice caused by atmospheric mineral dust – Part 1 11377

DeMott, P. J., Sassen, K., Poellot, M. R., Baumgardner, D., Rogers,D. C., Brooks, S. D., Prenni, A. J., and Kreidenweis, S. M.:African dust aerosols as atmospheric ice nuclei, Geophys. Res.Lett., 30, 1732, doi:10.1029/2003GL017410, 2003.

DeMott, P. J., Prenni, A. J., Liu, X., Petters, M. D., Twohy, C.H., Richardson, M. S., Eidhammer, T., Kreidenweis, S. M., andRogers, D. C.: Predicting global atmospheric ice nuclei distribu-tions and their impacts on climate, P. Natl. Acad. Sci. USA, 107,11217–11222, 2010.

De Mott, P. J., Möhler, O., Stetzer, O., Vali, G., Levin, Z., Petters,M. D., Murakami, M., Leisner, T., Bundke, U., Klein, H., Kanji,Z. A., Cotton, R., Jones, H., Benz, S., Brinkmann, M., Rzesanke,D., Saathoff, H., Nicolet, M., Saito, A., Nillius, B., Bingemer, H.,Abbatt, J., Ardon, K., Ganor, E., Georgakopoulos, D., and Saun-ders, C.: Resurgence in ice nuclei measurement research, B. Am.Meteorol. Soc., 92, 1623–1635, doi:10.1175/2011BAMS3119.1,2011.

DeMott, P. J., Prenni, A. J., McMeeking, G. R., Sullivan, R. C.,Petters, M. D., Tobo, Y., Niemand, M., Möhler, O., Snider, J.R., Wang, Z., and Kreidenweis, S. M.: Integrating laboratory andfield data to quantify the immersion freezing ice nucleation activ-ity of mineral dust particles, Atmos. Chem. Phys., 15, 393–409,doi:10.5194/acp-15-393-2015, 2015.

Eidhammer, T., DeMott, P. J., Prenni, A. J., Petters, M. D., Twohy,C. H., Rogers, D. C., Stith, J., Heymsfield, A., Wang, Z., Pratt, K.A., Prather, K. A., Murphy, S. M., Seinfeld, J. H., Subramanian,R., and Kreidenweis, S. M.: Ice Initiation by Aerosol Particles:Measured and Predicted Ice Nuclei Concentrations versus Mea-sured Ice Crystal Concentrations in an Orographic Wave Cloud,J. Atmos. Sci., 67, 2417–2436, 2010.

Fan, J., Leung, L. R., DeMott, P. J., Comstock, J. M., Singh, B.,Rosenfeld, D., Tomlinson, J. M., White, A., Prather, K. A., Min-nis, P., Ayers, J. K., and Min, Q.: Aerosol impacts on Californiawinter clouds and precipitation during CalWater 2011: local pol-lution versus long-range transported dust, Atmos. Chem. Phys.,14, 81–101, doi:10.5194/acp-14-81-2014, 2014.

Ferrier, B. S., Jin, Y., Lin, Y., Black, T., Rogers, E., and DiMego,G.: Implementation of a new grid-scale cloud and precipitationscheme in the NCEP Eta Model. Proc. 15th Conf. on NumericalWeather Prediction, San Antonio, TX, Am. Meteorol. Soc., 280–283, 2002.

Field, P. R., Heymsfield, A. J., Shipway, B. J., DeMott, P. J.,Pratt, K. A., Rogers, D. C., Stith, J., and Prather, K. A.: Ice inClouds Experiment-Layer Clouds, Part II: Testing Characteris-tics of Heterogeneous Ice Formation in Lee Wave Clouds, J. At-mos. Sci., 69, 1066–1079, 2012.

Fletcher, N. H.: The Physics of Rainclouds, 390 pp., CambridgeUniv. Press, Cambridge, UK, 1962.

Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B.,Dubovik, O., and Lin, S.: Sources and distributions of dustaerosols simulated with the GOCART model, J. Geophys. Res.,106, 20255–20273, 2001.

Groß, S., Freudenthaler, V., Schepanski, K., Toledano, C., Schäfler,A., Ansmann, A., and Weinzierl, B.: Optical properties of long-range transported Saharan dust over Barbados as measuredby dual-wavelength depolarization Raman lidar measurements,Atmos. Chem. Phys., 15, 11067–11080, doi:10.5194/acp-15-11067-2015, 2015.

Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Seasonal vari-ability of Saharan desert dust and ice nucleating particles overEurope, Atmos. Chem. Phys., 15, 4389–4397, doi:10.5194/acp-15-4389-2015, 2015.

Hogan, R. J., Mittermaier, M. P., and Illingworth, A. J.: The retrievalof ice water content from radar reflectivity factor and tempera-ture and its use in the evaluation of a mesoscale model, J. Appl.Meteorol. Clim., 45, 301–317, 2005.

Huang, J., Wang, T., Wang, W., Li, Z., and Yan, H.: Cli-mate effects of dust aerosols over East Asian arid and semi-arid regions, J. Geophys. Res.-Atmos., 119, 11398–11416,doi:10.1002/2014JD021796, 2014.

Janjic, Z. I.: The step-mountain eta coordinate model: further de-velopments of the convection, viscous sublayer and turbulenceclosure schemes, Mon. Weather Rev., 122, 927–945, 1994.

Janjic, Z. I.: Comments on “Development and Evaluation of a Con-vection Scheme for Use in Climate Models”, J. Atmos. Sci., 57,3686–3686, 2000.

Janjic, Z. I.: A Nonhydrostatic Model Based on a New Approach,Meteorol. Atmos. Phys., 82, 271–285, doi:10.1007/s00703-001-0587-6, 2003.

Janjic, Z. I., Gerrity Jr., J. P., and Nickovic, S.: An Alternative Ap-proach to Nonhydrostatic Modeling, Mon. Weather Rev., 129,1164–1178, 2001.

Janjic, Z., Janjic, T., and Vasic, R.: A class of conservative fourth-order advection schemes and impact of enhanced formal ac-curacy on extended-range forecasts, Mon. Weather Rev., 139,1556–1568, doi:10.1175/2010MWR3448.1, 2011.

Kirtman, B. P., Min, D., Infanti, J. M., Kinter III, J. L., Paolino,D. A., Zhang, Q., van den Dool, H., Saha, S., Mendezm, M.P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G.,Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert,S. D., Rienecker, M., Suarez, M., Li, Z. E., Marshak, J., Lim,Y., Tribbia, J., Pegion, K., Merryfield, W. J., Denis, B., andWood, E. F.: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; Phase-2 toward developingintraseasonal prediction, B. Am. Meteorol. Soc., 95, 585–601,doi:10.1175/BAMS-D-12-00050.1, 2014.

Madonna, F., Amodeo, A., D’Amico, G., Mona, L., and Pap-palardo, G.: Observation of non-spherical ultragiant aerosolusing a microwave radar, Geophys. Res. Lett., 37, L21814,doi:10.1029/2010GL044999, 2010.

Madonna, F., Amodeo, A., Boselli, A., Cornacchia, C., Cuomo,V., D’Amico, G., Giunta, A., Mona, L., and Pappalardo, G.:CIAO: the CNR-IMAA advanced observatory for atmosphericresearch, Atmos. Meas. Tech., 4, 1191–1208, doi:10.5194/amt-4-1191-2011, 2011.

Meyers, M. P., DeMott P. J., and Cotton, W. R.: New primary icenucleation parameterizations in an explicit cloud model, J. Appl.Meteorol., 31, 708–721, 1992.

Mona, L., Amodeo, A., D’Amico, G., Giunta, A., Madonna, F., andPappalardo, G.: Multi-wavelength Raman lidar observations ofthe Eyjafjallajökull volcanic cloud over Potenza, southern Italy,Atmos. Chem. Phys., 12, 2229–2244, doi:10.5194/acp-12-2229-2012, 2012.

Niemand, M., Moehler, O., Vogel, B., Vogel, H., Hoose, C., Con-nolly, P., Klein, H., Bingemer, H., DeMott, P., Skrotzki, J., andLeisner, T.: Parameterization of immersion freezing on mineral

www.atmos-chem-phys.net/16/11367/2016/ Atmos. Chem. Phys., 16, 11367–11378, 2016

11378 S. Nickovic et al.: Cloud ice caused by atmospheric mineral dust – Part 1

dust particles: An application in a regional scale model, J. At-mos. Sci., 69, 3077–3092, 2012.

Nickovic, S.: Distribution of dust mass over particle sizes: impactson atmospheric optics, Forth ADEC Workshop – Aeolian DustExperiment on Climate Impact, 26–28 January, Nagasaki, Japan,357–360, 2005.

Nickovic, S., Kallos, G., Papadopoulos, A., and Kakaliagou, O.: Amodel for prediction of desert dust cycle in the atmosphere, J.Geophys. Res., 106, 18113–18130, 2001.

Pappalardo, G., Mona, L., D’Amico, G., Wandinger, U., Adam, M.,Amodeo, A., Ansmann, A., Apituley, A., Alados Arboledas, L.,Balis, D., Boselli, A., Bravo-Aranda, J. A., Chaikovsky, A., Com-eron, A., Cuesta, J., De Tomasi, F., Freudenthaler, V., Gausa,M., Giannakaki, E., Giehl, H., Giunta, A., Grigorov, I., Groß,S., Haeffelin, M., Hiebsch, A., Iarlori, M., Lange, D., Linné,H., Madonna, F., Mattis, I., Mamouri, R.-E., McAuliffe, M. A.P., Mitev, V., Molero, F., Navas-Guzman, F., Nicolae, D., Pa-payannis, A., Perrone, M. R., Pietras, C., Pietruczuk, A., Pisani,G., Preißler, J., Pujadas, M., Rizi, V., Ruth, A. A., Schmidt, J.,Schnell, F., Seifert, P., Serikov, I., Sicard, M., Simeonov, V.,Spinelli, N., Stebel, K., Tesche, M., Trickl, T., Wang, X., Wag-ner, F., Wiegner, M., and Wilson, K. M.: Four-dimensional dis-tribution of the 2010 Eyjafjallajökull volcanic cloud over Europeobserved by EARLINET, Atmos. Chem. Phys., 13, 4429–4450,doi:10.5194/acp-13-4429-2013, 2013.

Pappalardo, G., Amodeo, A., Apituley, A., Comeron, A., Freuden-thaler, V., Linné, H., Ansmann, A., Bösenberg, J., D’Amico,G., Mattis, I., Mona, L., Wandinger, U., Amiridis, V., Alados-Arboledas, L., Nicolae, D., and Wiegner, M.: EARLINET: to-wards an advanced sustainable European aerosol lidar network,Atmos. Meas. Tech., 7, 2389–2409, doi:10.5194/amt-7-2389-2014, 2014.

Pejanovic, G., Nickovic, S., Vujadinovic, M., Vukovic, A., Djurd-jevic, V., and Dacic, M.: Atmospheric deposition of minerals indust over the open ocean and possible consequences on climate.WCRP OSC Climate Research in Service to Society, 24–28 Oc-tober 2011, Denver, CO, USA, 2011.

Phillips, V. T. J., DeMott, P. J., Andronache, C., Pratt, K., Prather,K. A., Subramanian, R., and Twohy, C.: Improvements to an Em-pirical Parameterization of Heterogeneous Ice Nucleation andits Comparison with Observations, J. Atmos. Sci., 70, 378–409,2013.

Rosenfeld, D., Yu, X., Liu, G., Xu, X., Zhu, Y., Yue, Z., Dai,J., Dong, Z., Dong, Y., and Peng, Y.: Glaciation tempera-tures of convective clouds ingesting desert dust, air pollutionand smoke from forest fires, Geophys. Res. Lett., 38, L21804,doi:10.1029/2011GL049423, 2011..

Rosoldi, M., Madonna, F., Claramunt, P. G., and Pappalardo, G.:Study of droplet activation in thin clouds using ground-basedRaman lidar and ancillary remote sensors, Proc. of the 27th In-ternational Laser Radar Conference (ILRC 27), New York City,USA, 5–10 July 2015, EPJ Web of Conferences, 119, 16008,doi:10.1051/epjconf/201611916008, 2016.

Sassen K.: Dusty ice clouds over Alaska, Nature, 434, 456,doi:10.1038/434456a, 2005.

Simmel, M., Bühl, J., Ansmann, A., and Tegen, I.: Ice phase inaltocumulus clouds over Leipzig: remote sensing observationsand detailed modeling, Atmos. Chem. Phys., 15, 10453–10470,doi:10.5194/acp-15-10453-2015, 2015.

Steinke, I., Hoose, C., Möhler, O., Connolly, P., and Leisner, T.:A new temperature- and humidity-dependent surface site densityapproach for deposition ice nucleation, Atmos. Chem. Phys., 15,3703–3717, doi:10.5194/acp-15-3703-2015, 2015.

Stengel, M., Kniffka, A., Meirink, J. F., Riihelä, A., Trentmann, J.,Müller, R., Lockhoff, M., and Hollmann, R.: CLAAS: CM SAFCLoud property dAtAset using SEVIRI, 1st Edn., Hourly/DailyMeans, Pentad Means, Monthly Means/Monthly Mean DiurnalCycle/Monthly Histograms, Satellite Application Facility on Cli-mate Monitoring, doi:10.5676/EUM_SAF_CM/CLAAS/V001,2013.

Stengel, M., Kniffka, A., Meirink, J. F., Lockhoff, M., Tan,J., and Hollmann, R.: CLAAS: the CM SAF cloud propertydata set using SEVIRI, Atmos. Chem. Phys., 14, 4297–4311,doi:10.5194/acp-14-4297-2014, 2014.

Teller, A., Xue, L., and Levin, Z.: The effects of mineral dust parti-cles, aerosol regeneration and ice nucleation parameterizationson clouds and precipitation, Atmos. Chem. Phys., 12, 9303–9320, doi:10.5194/acp-12-9303-2012, 2012.

Thompson, G. and Eidhammer, T.: A Study of Aerosol Impacts onClouds and Precipitation Development in a Large Winter Cy-clone, J. Atmos. Sci., 71, 3636–3658, 2014.

Tobo, Y., Prenni, A. J., DeMott, P. J., Huffman, J. A., McCluskey,C. S., Tian, G., Pöhlker, C., Pöschl, U., and Kreidenweis, S. M.:Biological aerosol particles as a key determinant of ice nucleipopulations in a forest ecosystem, J. Geophys. Res.-Atmos., 118,10100–10110, doi:10.1002/jgrd.50801, 2013.

Yakobi-Hancock, J. D., Ladino, L. A., and Abbatt, J. P. D.: Feldsparminerals as efficient deposition ice nuclei, Atmos. Chem. Phys.,13, 11175–11185, doi:10.5194/acp-13-11175-2013, 2013.

Wang, M., Liu, X., Zhang, K., and Comstock, J. M.: Aerosol effectson cirrus through ice nucleation in the Community AtmosphereModel CAM5 with a statistical cirrus scheme, J. Adv. Model.Earth Syst., 6, 756–776, 2014.

WMO: Recommendations for the Verification and Intercomparisonof QPFs and PQPFs from Operational NWP Models (Revision 2,October 2008) (WMO TD No. 1485) (WWRP 2009-1), 2009.

Atmos. Chem. Phys., 16, 11367–11378, 2016 www.atmos-chem-phys.net/16/11367/2016/