H. Portin

Long-term measurements of cloud droplet concentrations and aerosol–cloud interactions in continental boundary layer clouds

The effects of aerosol on cloud droplet effective radius (R eff), cloud optical thickness and cloud droplet number concentration (N d) are analysed both from long-term direct ground-based in situ measurements conducted at the Puijo measurement station in Eastern Finland and from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra and Aqua satellites. The mean in situ N d during the period of study was 217 cm−3, while the MODIS-based N d was 171 cm−3. The absolute values, and the dependence of both N d observations on the measured aerosol number concentration in the accumulation mode (N acc), are quite similar. In both data sets N d is clearly dependent on N acc, for N acc values lower than approximately 450 cm−3. Also, the values of the aerosol–cloud-interaction parameter [ACI=(1/3)*d ln(N d)/d ln(N acc)] are quite similar for N acc<400 cm−3 with values of 0.16 and 0.14 from in situ and MODIS measurements, respectively. With higher N acc (>450 cm−3) N d increases only slowly. Similarly, the effect of aerosol on MODIS-retrieved R eff is visible only at low N acc values. In a sub set of data, the cloud and aerosol properties were measured simultaneously. For that data the comparison between MODIS-derived N d and directly measured N d, or the cloud droplet number concentration estimated from N acc values (N d,p), shows a correlation, which is greatly improved after careful screening using a ceilometer to make sure that only single cloud layers existed. This suggests that such determination of the number of cloud layers is very important when trying to match ground-based measurements to MODIS measurements.

Aerosol Chemical Composition in Cloud Events by High Resolution Time-of-Flight Aerosol Mass Spectrometry

This study presents results of direct observations of aerosol chemical composition in clouds. A high-resolution time-of-flight aerosol mass spectrometer was used to make measurements of cloud interstitial particles (INT) and mixed cloud interstitial and droplet residual particles (TOT). The differences between these two are the cloud droplet residuals (RES). Positive matrix factorization analysis of high-resolution mass spectral data sets and theoretical calculations were performed to yield distributions of chemical composition of the INT and RES particles. We observed that less oxidized hydrocarbon-like organic aerosols (HOA) were mainly distributed into the INT particles, whereas more oxidized low-volatile oxygenated OA (LVOOA) mainly in the RES particles. Nitrates existed as organic nitrate and in chemical form of NH(4)NO(3). Organic nitrates accounted for 45% of total nitrates in the INT particles, in clear contrast to 26% in the RES particles. Meanwhile, sulfates coexist in forms of acidic NH(4)HSO(4) and neutralized (NH(4))(2)SO(4). Acidic sulfate made up 64.8% of total sulfates in the INT particles, much higher than 10.7% in the RES particles. The results indicate a possible joint effect of activation ability of aerosol particles, cloud processing, and particle size effects on cloud formation.

Representing situational knowledge acquired from sensor data for atmospheric phenomena

Abstract A recurrent problem in applications that build on environmental sensor networks is that of sensor data organization and interpretation. Organization focuses on, for instance, resolving the syntactic and semantic heterogeneity of sensor data. The distinguishing factor between organization and interpretation is the abstraction from sensor data with information acquired from sensor data. Such information may be situational knowledge for environmental phenomena. We discuss a generic software framework for the organization and interpretation of sensor data and demonstrate its application to data of a large scale sensor network for the monitoring of atmospheric phenomena. The results show that software support for the organization and interpretation of sensor data is valuable to scientists in scientific computing workflows. Explicitly represented situational knowledge is also useful to client software systems as it can be queried, integrated, reasoned, visualized, or annotated.