ALBERT

Description: We present recent progress related to the night-time retrievals of aerosol and cloud optical depth using starphotometry over the PEARL (Polar Environmental Atmospheric Research Laboratory) station at Eureka (Nunavut, Canada) in the High Arctic (80° N, 86° W). In the spring of 2011 and 2012, the SPSTAR starphotometer was employed to acquire aerosol optical depth (AOD) measurements while vertical aerosol and cloud backscatter coefficient profiles were acquired using the CANDAC Raman Lidar (CRL). Several events were detected and characterized using starphotometry-lidar synergy: aerosols (short term aerosol events on 9 and 10 March 2011); a potential multi-night aerosol event across three polar nights (13–15 March 2012), a thin cloud event (21 February 2011) and a very low altitude ice crystals (10 March 2011). Using a simple backscatter coefficient threshold criterion we calculated fine and coarse (sub and super-micron) mode AODs from the vertically integrated CRL profiles. These were compared with their starphotometry analogues produced from a spectral deconvolution algorithm. The process-level analysis showed, in general, good agreement in terms of the physical coherence between high frequency starphotometry and lidar data. We argue that R2 (coefficient of determination) is the most robust means of comparing lidar and starphotometer data since it is sensitive to significant optico-physical variations associated with these two independent data sources while being minimally dependent on retrieval and calibration artifacts. Differences between the fine and course mode components of the starphotometry and lidar data is clearly also useful but is more dependent on such artifacts. Studying climatological seasonal aerosol trends necessitates effective cloud-screening procedures: temporal and spectral cloud screening of starphotometry data was found to agree moderately well with temporal cloud screening results except in the presence of thin homogeneous cloud. We conclude that better screening conditions can be implemented to arrive at a robust method for combined temporal/spectral cloud-screening of starphotometer (and possibly sunphotometer) data. In general, as our understanding of process-level details increases with growing datasets, we will inevitably have more confidence in bulk climatological analyses of ground-based and satellite retrievals of aerosol parameters where conditions are less than ideal because of the weakness of the polar winter aerosol signal.

Description: A far infrared radiometer (FIRR) dedicated to measure radiation emitted by clear and cloudy atmospheres was developed as a breadboard for the Thin Ice Clouds in Far InfraRed Experiment (TICFIRE) satellite project. The FIRR detector is an array of 80×60 uncooled microbolometers coated with gold black to enhance the absorptivity and responsivity. A filter wheel is used to select atmospheric radiation in 9 spectral bands ranging from 8 to 50 µm. Calibrated radiances are obtained using two well-calibrated blackbodies. Images are acquired at a frame rate of 120 Hz, and temporally averaged to reduce electronic noise. A complete measurements sequence takes about 120 seconds. With a field-of view of 6°, the FIRR is not intended to be an imager. Hence spatial average is computed over 193 illuminated pixels to increase the signal-to-noise ratio and consequently the detector resolution. This results in an improvement by a factor of 5 compared to individual pixel measurements. Another threefold increase in resolution is obtained using 193 non-illuminated pixels to remove correlated electronic noise, leading an overall resolution of approximately 0.015 W m–2 sr–1. Laboratory measurements performed on well known targets suggest an absolute accuracy close to 0.02 W m–2 sr–1, which ensures to retrieve atmospheric radiance with an accuracy better than 1%. Preliminary in situ experiments performed from the ground in winter and in summer on clear and cloudy atmospheres are compared to radiative transfer simulations. They point out the FIRR ability to detect clouds and changes in relative humidity of a few percent in various atmospheric conditions, paving the way for the development of new algorithms dedicated to ice cloud characterization and water vapor retrieval.

Description: We present recent progress on nighttime retrievals of aerosol and cloud optical properties over the PEARL (Polar Environmental Atmospheric Research Laboratory) station at Eureka (Nunavut, Canada) in the High Arctic (80° N, 86° W). In the spring of 2011 and 2012, a star photometer was employed to acquire aerosol optical depth (AOD) data, while vertical aerosol and cloud backscatter profiles were measured using the CANDAC Raman Lidar (CRL). We used a simple backscatter coefficient threshold (βthr) to distinguish aerosols from clouds and, assuming that aerosols were largely fine mode (FM)/sub-micron, to distinguish FM aerosols from coarse mode (CM)/super-micron cloud or crystal particles. Using prescribed lidar ratios, we computed FM and CM AODs that were compared with analogous AODs estimated from spectral star photometry. We found (βthr dependent) coherences between the lidar and star photometer for both FM events and CM cloud and crystal events with averaged, FM absolute differences being

Description: We present recent progress related to the night-time retrievals of aerosol and cloud optical depth using starphotometry over the PEARL (Polar Environmental Atmospheric Research Laboratory) station at Eureka (Nunavut, Canada) in the High Arctic (80° N, 86° W). In the spring of 2011 and 2012, the SPSTAR starphotometer was employed to acquire aerosol optical depth (AOD) measurements while vertical aerosol and cloud backscatter coefficient profiles were acquired using the CANDAC Raman Lidar (CRL). Several events were detected and characterized using starphotometry-lidar synergy: aerosols (short term aerosol events on 9 and 10 March 2011); a potential multi-night aerosol event across three polar nights (13–15 March 2012), a thin cloud event (21 February 2011) and a very low altitude ice crystals (10 March 2011). Using a simple backscatter coefficient threshold criterion we calculated fine and coarse (sub and super-micron) mode AODs from the vertically integrated CRL profiles. These were compared with their starphotometry analogues produced from a spectral deconvolution algorithm. The process-level analysis showed, in general, good agreement in terms of the physical coherence between high frequency starphotometry and lidar data. We argue that R2 (coefficient of determination) is the most robust means of comparing lidar and starphotometer data since it is sensitive to significant optico-physical variations associated with these two independent data sources while being minimally dependent on retrieval and calibration artifacts. Differences between the fine and course mode components of the starphotometry and lidar data is clearly also useful but is more dependent on such artifacts. Studying climatological seasonal aerosol trends necessitates effective cloud-screening procedures: temporal and spectral cloud screening of starphotometry data was found to agree moderately well with temporal cloud screening results except in the presence of thin homogeneous cloud. We conclude that better screening conditions can be implemented to arrive at a robust method for combined temporal/spectral cloud-screening of starphotometer (and possibly sunphotometer) data. In general, as our understanding of process-level details increases with growing datasets, we will inevitably have more confidence in bulk climatological analyses of ground-based and satellite retrievals of aerosol parameters where conditions are less than ideal because of the weakness of the polar winter aerosol signal.