Comparison of SAFNWC/MSG Satellite Cloud Type with Vaisala CL51 Ceilometer-Detected Cloud Base Layer Using the Sky Condition Algorithm and Vaisala BL-View Software

Šálek, Milan; Szabó-Takács, Beáta

doi:10.3390/atmos10060316

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For cloud-base heights we use the Vaisala ceilometer's "sky-condition" algorithm, incorporating data from 30 min with larger weight on the last 10 min (e.g. Šálek and Szabó-Takács, 2019). For soundings we use actual launch time, disregarding the time it takes for the balloon to ascend through the troposphere and its horizontal displacement.…”

Section: Error Analysismentioning

confidence: 99%

Central Arctic weather forecasting: Confronting theECMWF IFSwith observations from the Arctic Ocean 2018 expedition

Tjernström

Svensson

Magnusson

et al. 2021

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Forecasts with the European Centre for Medium‐Range Weather Forecasts' numerical weather prediction model are evaluated using an extensive set of observations from the Arctic Ocean 2018 expedition on the Swedish icebreaker Oden. The atmospheric model (Cy45r1) is similar to that used for the ERA5 reanalysis (Cy41r2). The evaluation covers 1 month, with the icebreaker moored to drifting sea ice near the North Pole; a total of 125 forecasts issued four times per day were used. Standard surface observations and 6‐hourly soundings were assimilated to ensure that the initial model error is small. Model errors can be divided into two groups. First, variables related to dynamics feature errors that grow with forecast length; error spread also grows with time. Initial errors are small, facilitating a robust evaluation of the second group; thermodynamic variables. These feature fast error growth for 6–12 hr, after which errors saturates; error spread is roughly constant. Both surface and near‐surface air temperatures are too warm in the model. During the summer both are typically above zero in spite of the ongoing melt; however, the warm bias increases as the surface freezes. The warm bias is due to a too warm atmosphere; errors in surface sensible heat flux transfer additional heat from the atmosphere to the surface. The lower troposphere temperature error has a distinct vertical structure: a substantial warm bias in the lowest few 100 m and a large cold bias around 1 km; this structure features a significant diurnal cycle and is tightly coupled to errors in the modelled clouds. Clouds appear too often and in a too deep layer of the lower atmosphere; the lowest clouds essentially never break up. The largest error in cloud presence is aligned with the largest cold bias at around 1 km.

show abstract

Section: Error Analysismentioning

confidence: 99%

Central Arctic weather forecasting: Confronting theECMWF IFSwith observations from the Arctic Ocean 2018 expedition

Tjernström

Svensson

Magnusson

et al. 2021

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

show abstract

“…As the name implied, the ceilometer was used originally to determine the cloud base using lidar. Cloud base heights, as detected by ceilometers, were recently used to classify cloud base types at different heights, and compared with observed satellite cloud types with high-cloud-type detection rates of 80% in winter and 71% in summer, even when low and medium heigh cloud were present below [6]. Since 2008, aerosol layers have been included in the investigation of backscattering lidar, as ceilometers reflect every particle, including rain droplets, fogs, moisture droplets, and aerosols.…”

Section: Introductionmentioning

confidence: 99%

Study of Planetary Boundary Layer, Air Pollution, Air Quality Models and Aerosol Transport Using Ceilometers in New South Wales (NSW), Australia

Rahman

Trieu

et al. 2022

Atmosphere

View full text Add to dashboard Cite

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and, hence, the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometers, a lidar instrument to measure cloud base height, have been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ), as well as to understand the aerosol transport from sources to the receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM10, PM2.5, NO2), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate.

show abstract

“…As the name implied, the ceilometer was used originally to determine the cloud base using lidar. Cloud base heights as detected by ceilometer was recently compared with satellite cloud type with good accuracy [6]. Since 2008, aerosol layers were included in the investigation of backscattering lidar as ceilometer reflects every particle including, rain droplets, fogs, moisture droplets, aerosols… A ceilometer measures the optical backscatter intensity in the air that depends on the particle concentration in the air.…”

Section: Introductionmentioning

confidence: 99%

Study of Planetary Boundary Layer and Aerosol Transport Using Ceilometer and Air Quality Modelling in New South Wales (NSW), Australia

H.¹,

Raman²,

Trieu³

et al. 2021

Preprint

View full text Add to dashboard Cite

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and hence the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometer, a lidar instrument to measure cloud base height, has been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in the New South Wales, Australia was used to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ) as well as to understand the aerosol transport from sources to receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results show that the PBLH prediction by the two air quality models corresponds reasonably well with observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate with back trajectory analysis of transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol source from north and north west of Merriwa in remote NSW where windblown dust is the main source, and the other two had sources from south and south east of Merriwa where anthropogenic sources dominate,

show abstract

Comparison of SAFNWC/MSG Satellite Cloud Type with Vaisala CL51 Ceilometer-Detected Cloud Base Layer Using the Sky Condition Algorithm and Vaisala BL-View Software

Cited by 6 publications

References 14 publications

Central Arctic weather forecasting: Confronting theECMWF IFSwith observations from the Arctic Ocean 2018 expedition

Central Arctic weather forecasting: Confronting theECMWF IFSwith observations from the Arctic Ocean 2018 expedition

Study of Planetary Boundary Layer, Air Pollution, Air Quality Models and Aerosol Transport Using Ceilometers in New South Wales (NSW), Australia

Study of Planetary Boundary Layer and Aerosol Transport Using Ceilometer and Air Quality Modelling in New South Wales (NSW), Australia

Contact Info

Product

Resources

About