ALBERT

Description: Measurement of water vapor or humidity in the atmosphere is fundamental for many applications. Relative humidity measurements with a capacitive sensor in radiosondes are affected by several factors that need to be assessed and corrected. This work aims to address corrections for the main effects for the Meteomodem M10 radiosonde as a step to meet the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) requirements. The considered corrections are 1) the calibration correction; 2) a slow regime due to the slow diffusion of molecules through the sensor, especially at very high and very low relative humidity conditions; 3) the relative humidity sensor dependence on the gradient of temperature; and 4) the time lag at cold temperatures, which affects measurements in regions of strong relative humidity gradients. These corrections were tested for 26 nighttime and 25 daytime radiosondes in two midlatitude locations for which both Meteomodem M10 and Vaisala RS92 measurements were available. The results show that, after correcting for the four effects, M10 relative humidity measurements are, on average, consistent with the Vaisala RS92 relative humidity values within 2% RH at all altitudes for the nighttime launches (against 6% RH before the correction) and within 5% RH at all altitudes for the daytime launches (against 9% RH before the correction).

Description: 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.

Description: The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index (Kc), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and Kc. Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed Kc in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods.

Description: 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

Description: This article presents a new cloud radar calibration methodology using solid reference reflectors mounted on masts, developed during two field experiments held in 2018 and 2019 at the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA) atmospheric observatory, located in Palaiseau, France, in the framework of the Aerosol Clouds Trace gases Research InfraStructure version 2 (ACTRIS-2) research and innovation program. The experimental setup includes 10 and 20 cm triangular trihedral targets installed at the top of 10 and 20 m masts, respectively. The 10 cm target is mounted on a pan-tilt motor at the top of the 10 m mast to precisely align its boresight with the radar beam. Sources of calibration bias and uncertainty are identified and quantified. Specifically, this work assesses the impact of receiver compression, temperature variations inside the radar, frequency-dependent losses in the receiver's intermediate frequency (IF), clutter and experimental setup misalignment. Setup misalignment is a source of bias, previously undocumented in the literature, that can have an impact of the order of tenths of a decibel in calibration retrievals of W-band radars. A detailed analysis enabled the quantification of the importance of each uncertainty source to the final cloud radar calibration uncertainty. The dominant uncertainty source comes from the uncharacterized reference target which reached 2 dB. Additionally, the analysis revealed that our 20 m mast setup with an approximate alignment approach is preferred to the 10 m mast setup with the motor-driven alignment system. The calibration uncertainty associated with signal-to-clutter ratio of the former is 10 times smaller than for the latter. Following the proposed methodology, it is possible to reduce the added contribution from all uncertainty terms, excluding the target characterization, down to 0.4 dB. Therefore, this procedure should enable the achievement of calibration uncertainties under 1 dB when characterized reflectors are available. Cloud radar calibration results are found to be repeatable when comparing results from a total of 18 independent tests. Once calibrated, the cloud radar provides valid reflectivity values when sampling midtropospheric clouds. Thus, we conclude that the method is repeatable and robust, and that the uncertainties are precisely characterized. The method can be implemented under different configurations as long as the proposed principles are respected. It could be extended to reference reflectors held by other lifting devices such as tethered balloons or unmanned aerial vehicles.

Description: The first Clouds and the Earth's Radiant Energy System (CERES) instrument has been returning useful data on Earth's radiation budget from the Tropical Rainfall Measuring Mission (TRMM) spacecraft since late 1997. Validation of the initial data is now intensively underway. As one component of this validation, the CERES Students' Cloud Observations On-Line (S'COOL) project has been operational since April 1998 - the 2nd CERES validation month. S'COOL involves school children in over 140 schools in 15 countries on 5 continents in making and reporting observations and measurements which they and CERES scientists can then compare to the satellite retrievals. The project is planned to continue through the life of the CERES Project (nominally 15 years), and new participants are invited to join on a continuous basis. This paper will report on the first year of the operational phase of the project, during which a number of exciting events occurred (a demonstration of the project to First Lady Hillary Rodham Clinton, and visits by CERES personnel to participating schools, among others). It will further report on some of the noteworthy observations and comparisons which have been made possible by this project. We have found that schools are often located in interesting places, in terms of the clouds found there and the satellite's ability to observe these clouds. The paper will also report on the learning opportunities delivered by this project, and on new questions about the planet and its climate which arise in the students' minds as a result of their active participation.

Description: The first Clouds and the Earth's Radiant Energy System (CERES) instrument has been returning useful data on Earth's radiation budget from the Tropical Rainfall Measuring Mission (TRMM) spacecraft since late 1997. Validation of the initial data is now intensively underway. As one component of this validation, the CERES Students Cloud Observations On-Line (S'COOL) project has been operational since April 1998 - the 2nd CERES validation month. S'COOL involves school children in over 140 schools in 15 countries on 5 continents in making and reporting observations and measurements which they and CERES scientists can then compare to the satellite retrievals. The project is planned to continue through the life of the CERES Project (nominally 15 years), and new participants are invited to join on a continuous basis. This paper will report on the first year of the operational phase of the project, during which a number of exciting events occurred (a demonstration of the project to First Lady Hillary Rodham Clinton, and visits by CERES personnel to participating schools, among others). It will further report on some of the noteworthy observations and comparisons which have been made possible by this project. We have found that schools are often located in interesting places, in terms of the clouds found there and the satellite's ability to observe these clouds. The paper will also report on the learning opportunities delivered by this project, and on new questions about the planet and its climate which arise in the students minds as a result of their active participation.

Description: Verner E. Soumi was the father of radiation budget measurements from space. He directed the team at the University of Wisconsin that developed the first radiation budget measurements on the Iota (Explorer VII) coverage) spacecraft in 1959. The first data published was from hand calculations of night-time long-wave fluxes, with absolute accuracy estimated as better than 10 percent, and the data shown as hand drawn maps with lines of equal "long-wave radiation loss, in Langleys per minute X 10(exp -3) (isolangleys)". The first comparisons of the new radiation data with nephanalyses showed that clouds dominated the radiation patterns. Soumi immediately proposed using the radiation fields to help understand the atmospheric heat sources necessary to drive the atmospheric circulation. This early work already pointed to the relationship between the outgoing longwave radiation at the top of the atmosphere and the vertical flux divergence of infrared radiation within the atmosphere. In the next 30 years, global satellite observations of the radiation balance of the planet have advanced both in accuracy, stability, and in their ability to address cause and effect in the climate system. The purpose of the present paper is to examine early results of the new Clouds and the Earth's Radiant Energy System (CERES) data on Tropical Rainfall Measuring System (TRMM) which started data collection in January, 1998. CERES is a direct descendant of the legacy of Soumi's foresight on understanding the global energetics using satellite observations of broadband radiation.