Classification of ice crystal shapes in midlatitude ice clouds from three years of lidar observations over the SIRTA observatory

This paper presents a study of ice crystal shapes in midlatitude ice clouds inferred from a technique based on the comparison of ray-tracing simulations with lidar depolarization ratio measured at 532 nm. This technique is applied to three years of lidar depolarization ratio observations from the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) observatory in Palaiseau, France, amounting to 322 different days of ice cloud observations. Particles in clouds are classified in three major groups : plates, columns, and irregular shapes with aspect ratios close to unity. Retrieved shapes are correlated with radiosounding observations from a close-by meteorological station: temperature, relative humidity, wind speed and direction

Improved Thin Cirrus and Terminator Cloud Detection in CERES Cloud Mask

Thin cirrus clouds account for about 20-30% of the total cloud coverage and affect the global radiation budget by increasing the Earth's albedo and reducing infrared emissions. Thin cirrus, however, are often underestimated by traditional satellite cloud detection algorithms. This difficulty is caused by the lack of spectral contrast between optically thin cirrus and the surface in techniques that use visible (0.65 micron ) and infrared (11 micron ) channels. In the Clouds and the Earth s Radiant Energy System (CERES) Aqua Edition 1 (AEd1) and Terra Edition 3 (TEd3) Cloud Masks, thin cirrus detection is significantly improved over both land and ocean using a technique that combines MODIS high-resolution measurements from the 1.38 and 11 micron channels and brightness temperature differences (BTDs) of 11-12, 8.5-11, and 3.7-11 micron channels. To account for humidity and view angle dependencies, empirical relationships were derived with observations from the 1.38 micron reflectance and the 11-12 and 8.5-11 micron BTDs using 70 granules of MODIS data in 2002 and 2003. Another challenge in global cloud detection algorithms occurs near the day/night terminator where information from the visible 0.65 micron channel and the estimated solar component of 3.7 micron channel becomes less reliable. As a result, clouds are often underestimated or misidentified near the terminator over land and ocean. Comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer [AVHRR]) cloud coverage and Geoscience Laser Altimeter System (GLAS) measurements north of 60 N indicate significant amounts of missing clouds from CLAVR-x because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products (MOD06) and GLAS in the same region also show similar difficulties with MODIS cloud retrievals. The consistent detection of clouds through out the day is needed to provide reliable cloud and radiation products for CERES and other research efforts involving the modeling of clouds and their interaction with the radiation budget

Experimental and Numerical Study of the Influence of String Mismatch on the Yield of PV Modules Augmented By Static Planar Reflectors

International audiencePhotovoltaic (PV) modules are generally installed by the application of empirical rules aimed at reducing shadows during the periods of high solar irradiation. A traditional installation on a horizontal surface results in largely spaced rows of modules with a relatively low tilt angle. The addition of inter-row reflectors results in more direct and diffuse flux transmitted to the cells. The " Aleph " (Amélioration de l'Efficacité Photovolta¨que) project aims to define clear rules for optimal settings of systems of PV module rows with fixed inter-row planar reflectors in a given location and under a given climate. Two PV technologies are tested for performance with this type of system: amorphous silicon (a-Si) and polycrystalline silicon (p-Si). This work combines experiments on panel behavior in an outdoor environment on the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) meteorology platform and a multiphysics numerical model used to couple all the important physical phenomena and accurately describe the system behavior. The model includes a ray tracing radiation/optics module based on the Monte-Carlo method, as well as an electrical module simulated in SPICE. This work presents the influence of the string mismatch losses, present at periods of heterogeneous illumination, on the yield of PV modules augmented by static planar reflectors

Midlatitude Cirrus Clouds and Multiple Tropopauses from a 2002-2006 Climatology over the SIRTA Observatory

This study present a comparison of lidar observations of midlatitude cirrus clouds over the SIRTA observatory between 2002 and 2006 with multiple tropopauses (MT) retrieved from radiosounding temperature profiles. The temporal variability of MT properties (frequency, thickness) are discussed. Results show a marked annual cycle, with MT frequency reaching its lowest point in May (~18% occurrence of MT) and slowly rising to more than 40% in DJF. The average thickness of the MT also follows an annual cycle, going from less than 1 km in spring to 1.5 km in late autumn. Comparison with lidar observations show that cirrus clouds show a preference for being located close below the 1st tropopause. When the cloud top is above the 1st tropopause (7% of observations), in 20% of cases the cloud base is above it as well, resulting in a cirrus cloud "sandwiched" between the two tropopauses. Compared to the general distribution of cirrus, cross-tropopause cirrus show a higher frequency of large optical depths, while inter-tropopause cirrus show almost exclusively low optical depths (Tau < 0.03 in 90% of cases) typical of subvisible clouds. Results suggest the occurrence of inter-tropopause cirrus clouds is correlated with the frequency of multiple tropopauses

Solar irradiances measured using SPN1 radiometers: uncertainties and clues for development

International audienceThe fast development of solar radiation and energy applications, such as photovoltaic and solar thermodynamic systems, has increased the need for solar radiation measure-ment and monitoring, for not only the global but also the diffuse and direct components. End users look for the best compromise between getting close to state-of-the-art mea-surements and keeping low capital, maintenance and operat-ing costs. Among the existing commercial options, SPN1 is a relatively low cost solar radiometer that estimates global and diffuse solar irradiances from seven thermopile sensors under a shading mask and without moving parts. This work presents a comprehensive study of SPN1 accu-racy and sources of uncertainty, drawing on laboratory ex-periments, numerical modelling and comparison studies be-tween measurements from this sensor and state-of-the art in-struments for six diverse sites. Several clues are provided for improving the SPN1 accuracy and agreement with state-of-the art measurements

Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers

Ceilometer lidars are used for cloud base height detection, to probe aerosol layers in the atmosphere (e.g. detection of elevated layers of Saharan dust or volcanic ash), and to examine boundary layer dynamics. Sensor optics and acquisition algorithms can strongly influence the observed attenuated backscatter profiles; therefore, physical interpretation of the profiles requires careful application of corrections. This study addresses the widely deployed Vaisala CL31 ceilometer. Attenuated backscatter profiles are studied to evaluate the impact of both the hardware generation and firmware version. In response to this work and discussion within the CL31/TOPROF user community (TOPROF, European COST Action aiming to harmonise ground-based remote sensing networks across Europe), Vaisala released new firmware (versions 1.72 and 2.03) for the CL31 sensors. These firmware versions are tested against previous versions, showing that several artificial features introduced by the data processing have been removed. Hence, it is recommended to use this recent firmware for analysing attenuated backscatter profiles. To allow for consistent processing of historic data, correction procedures have been developed that account for artefacts detected in data collected with older firmware. Furthermore, a procedure is proposed to determine and account for the instrument-related background signal from electronic and optical components. This is necessary for using attenuated backscatter observations from any CL31 ceilometer. Recommendations are made for the processing of attenuated backscatter observed with Vaisala CL31 sensors, including the estimation of noise which is not provided in the standard CL31 output. After taking these aspects into account, attenuated backscatter profiles from Vaisala CL31 ceilometers are considered capable of providing valuable information for a range of applications including atmospheric boundary layer studies, detection of elevated aerosol layers, and model verification

Meteorology-driven variability of air pollution (PM₁) revealed with explainable machine learning

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM1), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM1 at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM1 concentrations (coefficient of determination (R2) of 0.58), with model performance depending on the individual PM1 species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m3 for MLHs below <500 m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM1 concentrations of as much as ∼9 µg/m3. Northeasterly winds are found to contribute ∼5 µg/m3 to predicted PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM1 concentrations in summer are temperatures above ∼25 ∘C (contributions of up to ∼2.5 µg/m3), dry spells of several days (maximum contributions of ∼1.5 µg/m3), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m3), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures

Towards the profiling of the atmospheric boundary layer at European scale—introducing the COST Action PROBE

n/

Tailored algorithms for the detection of the atmospheric boundary layer height from common automatic lidars and ceilometers (ALC)

A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for the height of the ABL and its sublayers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Here, the advanced algorithm STRATfinder is presented for application to high signal-to-noise ratio (SNR) ALC observations, and results are compared to an automatic algorithm designed for low-SNR measurements (CABAM). The two algorithms are evaluated for application in an operational network setting. Results indicate that the ABL heights derived from low-SNR ALC have increased uncertainty during daytime deep convection, while high-SNR observations can have slightly reduced capabilities in detecting shallow nocturnal layers. Agreement between the ALC-based methods is similar when either is compared to the ABL heights derived from temperature profile data. The two independent methods describe very similar average diurnal and seasonal variations. Hence, high-quality products of ABL heights may soon become possible at national and continental scales

BASTA : a 95GHz FM-­‐CW Cloud radar

International audienceGround-based continuous observation of non-precipitating clouds and fo