Låga moln definieras av att ha molnbasen från 0 till 2 km ovanför markytan. Molnbildande bygger på att den relativa fuktigheten stiger med höjden tills vattenångan i luften kondenseras. Prognosmodeller för moln bygger på grundläggande termodynamiska och fluiddynamiska ekvationer. Områden delas in i ett rutnät och ekvationerna löses med numeriska metoder. För jämförelse kan mätinstrument samt observationer användas, såsom ceilometrar, radar eller observatörer.  Resultat från fyra olika experiment med prognosmodeller för moln används i detta arbete, som är en litteraturstudie för att undersöka modellers förmåga att simulera låga moln. Olika platser, på global och lokal skala, undersöks. Makroskopiska parametrar såsom molnandel och molnfrekvens är i fokus.  WRF-modellen fungerar bäst med 12 km horisontell upplösning, med en viss överskattning av molnfrekvensen. Modellen CAM5 simulerar molnandel väl men vatteninnehåll och isinnehåll underskattas respektive överskattas. Säsongscykler av låga moln fångas väl av modellerna ECMWF, ARPEGE, RACMO och Met Office, med viss överskattning från samtliga modeller. GFS-modellen överskattar molnandelen långt från ekvatorn med upp mot 80% men underskattar nära ekvatorn med 10–20%. Överskattningar och underskattningar kan bero på faktorer såsom otillräcklig representation av mikrofysik eller möjligtvis felaktiga mätdata. Det denna studie visar är däremot att prognosmodeller på lokal skala kan ge bra simuleringar av makroskopiska parametrar av låga moln. / Cloud types are defined by the height of their bases. Low-level clouds have cloud base heights between 0 and 2 km. They are formed when the relative humidity in the air reaches 100 %, leading to the formation of cloud droplets. Forecast models simulate clouds by integrating thermodynamic and fluid dynamic equations using numerical methods. Instruments and observations, such as ceilometers or observers, are used to assess the accuracy of these simulations.  This study uses four previous works, where forecast models have been used to forecast clouds, to study the accuracy of low-level cloud forecasts. This is done on both local and global scales, focusing on macroscopic characteristics such as cloud fractions and frequencies. The results show that the WRF model works best with a horizontal resolution of 12 km, with slight overestimation of cloud frequencies. The climate model CAM5 simulates cloud fractions well, but liquid- and ice content deviate significantly from measurements. Seasonal cycles are generated well by ECMWF, ARPEGE, RACMO and Met Office Unified Model, with reoccurring overestimations by all models. The GFS model overestimates cloud fractions in higher latitudes by up to 80%, but underestimates near the equator by 10-20%.  Lacking representation of microphysics in the models, or faulty data, can be the causes for deviations in the models. However, this study has shown that forecast models can simulate macroscopic parameters of low-level clouds on a local scale well.

<p>A study of air mass origin’s effect on marine stratus and stratocumulus clouds has been performed on clouds north of Scandinavia between 2000 and 2004. The aerosol number size distribution of the air masses has been obtained from measurements in northern Finland. A trajectory model has been used to calculate trajectories to and from the measurement stations. The back trajectories were calculated using the measurement site as receptor to make sure the air masses had the right origin, and forward trajectories were calculated from receptor stations to assure adequate flow conditions. Satellite data of microphysical parameters of clouds from the Moderate Resolution Imaging Spectrometer (MODIS) has been downloaded where the trajectories indicated that clouds could be studied, and where the satellite images displayed low-level clouds. The 25 % days with the highest number of aerosol with a diameter over 80 nm (N₈₀) and the 35% with the lowest N₈₀ have been used to represent polluted and clean conditions respectively. After screening trajectories and satellite imagery, 22 cases of clouds with northerly trajectories that had low N₈₀ values (i.e. clean) and 25 southerly cases with high N₈₀ values (i.e. polluted) where identified for further analysis.</p><p>   The average cloud optical thickness (τ) for all polluted pixels was more than twice that of the clean pixels. This can most likely be related to the differences in aerosol concentrations in accordance with the indirect effect, yet some difference in τ caused by different meteorological situations cannot be ruled out. The mean cloud droplet effective radius (a_ef) was for the polluted pixels 11.2 µm and for the clean pixels 15.5 µm, which results in a difference of 4.3 µm and clearly demonstrates the effect that increased aerosol numbers has on clouds. A non-linear relationship between a_ef and N₈₀ has been obtained which indicates that changes in lower values of aerosol numbers affect a_ef more than changes in larger aerosol loads. The results from this study also indicate that there is a larger difference in the microphysical cloud parameters between the polluted and clean cases in spring and autumn than in summer.</p>

A study of air mass origin’s effect on marine stratus and stratocumulus clouds has been performed on clouds north of Scandinavia between 2000 and 2004. The aerosol number size distribution of the air masses has been obtained from measurements in northern Finland. A trajectory model has been used to calculate trajectories to and from the measurement stations. The back trajectories were calculated using the measurement site as receptor to make sure the air masses had the right origin, and forward trajectories were calculated from receptor stations to assure adequate flow conditions. Satellite data of microphysical parameters of clouds from the Moderate Resolution Imaging Spectrometer (MODIS) has been downloaded where the trajectories indicated that clouds could be studied, and where the satellite images displayed low-level clouds. The 25 % days with the highest number of aerosol with a diameter over 80 nm (N80) and the 35% with the lowest N80 have been used to represent polluted and clean conditions respectively. After screening trajectories and satellite imagery, 22 cases of clouds with northerly trajectories that had low N80 values (i.e. clean) and 25 southerly cases with high N80 values (i.e. polluted) where identified for further analysis.    The average cloud optical thickness (τ) for all polluted pixels was more than twice that of the clean pixels. This can most likely be related to the differences in aerosol concentrations in accordance with the indirect effect, yet some difference in τ caused by different meteorological situations cannot be ruled out. The mean cloud droplet effective radius (aef) was for the polluted pixels 11.2 µm and for the clean pixels 15.5 µm, which results in a difference of 4.3 µm and clearly demonstrates the effect that increased aerosol numbers has on clouds. A non-linear relationship between aef and N80 has been obtained which indicates that changes in lower values of aerosol numbers affect aef more than changes in larger aerosol loads. The results from this study also indicate that there is a larger difference in the microphysical cloud parameters between the polluted and clean cases in spring and autumn than in summer.