Design choices for the prediction and optimization stage of finite-set model based predictive control

The interest in applying model-based predictive control (MBPC) for power-electronic converters has grown tremendously in the past years. This is due to the fact that MBPC allows fast and accurate control of multiple controlled variables for hybrid systems such as a power electronic converter and its load. As MBPC is a family of possible controllers rather than one single controller, several design choices are to be made when implementing MBPC. In this paper several conceptual possibilities are considered and compared for two important parts of online Finite-Set MBPC (FS-MBPC) algorithm: the cost function in the optimizations step and the prediction model in the prediction step. These possibilities are studied for two different applications of FS-MBPC for power electronics. The cost function is studied in the application of output current and capacitor voltage control of a 3-level flying-capacitor inverter. The aspect of the prediction model is studied for the stator flux and torque control of an induction machine with a 2-level inverter. The two different applications illustrate the versatility of FS-MBPC. In the study concerning the cost function firstly the comparison is made between quadratic and absolute value terms in the cost function. Comparable results are obtained, but a lower resource usage is obtained for the absolute value cost function. Secondly a capacitor voltage tracking control is compared to a control where the capacitor voltage may deviate without cost from the reference up to a certain voltage. The relaxed cost function results in better performance. For the prediction model both a classical, parametric machine model and a back propagation artificial neural network are applied. Both are shown to be capable of a good control quality, the neural network version is much more versatile but has a higher computational burden. However, the number of neurons in the hidden layer should be sufficiently high. All studied aspects were verified with experimental results and these validate the simulation results. Even more important is the fact that these experiments prove the feasibility of implementing online finite-set MBPC in an FPGA for both applications

The effect of flares on total solar irradiance

Flares are powerful energy releases occurring in stellar atmospheres. Solar flares, the most intense energy bursts in the solar system, are however hardly noticeable in the total solar luminosity. Consequently, the total amount of energy they radiate 1) remains largely unknown and 2) has been overlooked as a potential contributor to variations in the Total Solar Irradiance (TSI), i.e. the total solar flux received at Earth. Here, we report on the detection of the flare signal in the TSI even for moderate flares. We find that the total energy radiated by flares exceeds the soft X-ray emission by two orders of magnitude, with an important contribution in the visible domain. These results have implications for the physics of flares and the variability of our star.Comment: accepted in Nature Physic

Soil texture can predominantly control organic matter mineralization in temperate climates by regulating soil moisture rather than through direct stabilization

Soil organic carbon (OC) levels generally increase with increasing clay and silt content under a similar climatic zone because of increased association of OC to clay minerals and stronger occlusion inside aggregates. Surprisingly though, in Western Europe many silt loam soils actually bear low topsoil OC levels compared to lighter textured soils. Soil texture obviously also strongly controls moisture availability with consequent indirect impact on heterotrophic activity. We hypothesized that with increasingly frequent summer drought: 1) soil microbial activity in sandy soils is more likely impeded due to their limited water holding capacity retention during droughts, while soil OC mineralization in silty soils remain be less drought-limited; 2) capillary rise from sufficiently shallow groundwater would, on the other hand, alleviate the water stress in lighter textures. To test these hypotheses, we established a one-year field trial with manipulation of soil texture, monitoring of soil moisture and maize-C decomposition via 13/12C-CO2 emissions. The upper 0.5 m soil layer was replaced by sand, sandy loam and silt loam soil with low soil OC. Another sandy soil treatment with a gravel layer was also included beneath the sand layer to exclude capillary rise. Soil texture did not affect maize-C mineralization (Cmaize-min) until April 2019 and thereafter Cmaize-min rates were higher in the silt loam than in the sandy soils (P=0.01). θv correlated positively with the Cmaize-min rate for the sand-textured soils only but not for the finer textures. These results clearly highlight that soil texture controlled Cmaize-min indirectly through regulating moisture under the field conditions starting from about May, when soils faced a period of drought. By the end of the experiment, more added Cmaize was mineralized in the silt loam soil (81%) (P<0.05) than in the sandy soil (56%). Capillary rise did not result in a significant increase in cumulative Cmaize-min in the sandy soil, seemingly because the capillary fringe did not reach the sandy topsoil layer. These results imply that, under future climate scenarios the frequency of drought is expected to increase, the largely unimpeded microbial activity in silty soils might lead to a further stronger difference in soil OC with coarser textured soils under similar management

Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems

Decadal Changes of Earth’s Outgoing Longwave Radiation

The Earth Radiation Budget (ERB) at the top of the atmosphere quantifies how the earth gains energy from the sun and loses energy to space. Its monitoring is of fundamental importance for understanding ongoing climate change. In this paper, decadal changes of the Outgoing Longwave Radiation (OLR) as measured by the Clouds and Earth&rsquo;s Radiant Energy System from 2000 to 2018, the Earth Radiation Budget Experiment from 1985 to 1998, and the High-resolution Infrared Radiation Sounder from 1985 to 2018 are analysed. The OLR has been rising since 1985, and correlates well with the rising global temperature. An observational estimate of the derivative of the OLR with respect to temperature of 2.93 +/&minus; 0.3 W/m 2 K is obtained. The regional patterns of the observed OLR change from 1985&ndash;2000 to 2001&ndash;2017 show a warming pattern in the Northern Hemisphere in particular in the Arctic, as well as tropical cloudiness changes related to a strengthening of La Ni&ntilde;a

The Sun-Climate Connection Through Measurements and Modeling: The Picard Investigation

International audienc

Simultaneous measurement of the total solar irradiance and solar diameter by the PICARD mission

Gérard Thuilliera, , , Steven Dewitteb, Werner Schmutzc and The PICARD team aService d'Aéronomie du CNRS, Bp 3, 91371 Verrières-le-Buisson, France bRoyal Meteorological Institute of Belgium, 3 Avenue Circulaire, B-1180 Brussels, Belgium cPhysikalisch-Meteorologisches Observatorium Davos, World Radiation Center, CH-7260 Davos Dorf, Switzerland Received 7 February 2005; revised 14 April 2006; accepted 22 April 2006. Available online 18 September 2006. Abstract A mission dedicated to simultaneous measurements of the solar diameter, spectral, and total solar irradiance is presently in development for launch end of the year 2008 on board of a microsatellite under the responsibility of Centre National d'Etudes Spatiales. The payload will consist of an imaging telescope, three filter radiometers with in total twelve channels, and two independent absolute radiometers. The scientific aims are presented as well as the concepts and properties of the instrumentation. This mission is named PICARD after the pioneering work of Jean Picard (1620–1682) who precisely determined the solar diameter during the Maunder minimum

Centennial Total Solar Irradiance Variation

International audienceTotal Solar Irradiance (TSI) quantifies the solar energy received by the Earth and therefore is of direct relevance for a possible solar influence on climate change on Earth. We analyse the TSI space measurements from 1991 to 2021, and we derive a regression model that reproduces the measured daily TSI variations with a Root Mean Square Error (RMSE) of 0.17 W/m2. The daily TSI regression model uses the MgII core to wing ratio as a facular brightening proxy and the Photometric Sunspot Index (PSI) as a measure of sunspot darkening. We reconstruct the annual mean TSI backwards to 1700 based on the Sunspot Number (SN), calibrated on the space measurements with an RMSE of 0.086 W/m2. The analysis of the 11 year running mean TSI reconstruction confirms the existence of a 105 year Gleissberg cycle. The TSI level of the current grand minimum is only about 0.15 W/m2 higher than the TSI level of the grand minimum in the beginning of the 18th century

Decadal Changes of the Reflected Solar Radiation and the Earth Energy Imbalance

Decadal changes of the Reflected Solar Radiation (RSR) as measured by CERES from 2000 to 2018 are analysed. For both polar regions, changes of the clear-sky RSR correlate well with changes of the Sea Ice Extent. In the Arctic, sea ice is clearly melting, and as a result the earth is becoming darker under clear-sky conditions. However, the correlation between the global all-sky RSR and the polar clear-sky RSR changes is low. Moreover, the RSR and the Outgoing Longwave Radiation (OLR) changes are negatively correlated, so they partly cancel each other. The increase of the OLR is higher then the decrease of the RSR. Also the incoming solar radiation is decreasing. As a result, over the 2000&ndash;2018 period the Earth Energy Imbalance (EEI) appears to have a downward trend of &minus;0.16 &plusmn; 0.11 W/m2dec. The EEI trend agrees with a trend of the Ocean Heat Content Time Derivative of &minus;0.26 &plusmn; 0.06 (1 &sigma; ) W/m2dec

Dual View on Clear-Sky Top-of-Atmosphere Albedos from Meteosat Second Generation Satellites

Geostationary observations offer the unique opportunity to resolve the diurnal cycle of the Earth’s Radiation Budget at the top of the atmosphere (TOA), crucial for climate-change studies. However, a drawback of the continuous temporal coverage of the geostationary orbit is the fixed viewing geometry. As a consequence, imperfections in the angular distribution models (ADMs) used in the radiance-to-flux conversion process or residual angular-dependent narrowband-to-broadband conversion errors can result in systematic errors of the estimated radiative fluxes. In this work, focusing on clear-sky reflected TOA observations, we compare the overlapping views from Meteosat Second Generation satellites at 0° and 41.5°E longitude which enable a quantification of viewing-angle-dependent differences. Using data derived from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), we identify some of the main sources of discrepancies, and show that they can be significantly reduced at the level of one month. This is achieved, separately for each satellite, via a masking procedure followed by an empirical fit at the pixel-level that takes into account all the clear-sky data from that satellite, calculated separately per timeslot of the day, over the month of November 2016. The method is then applied to each month of 2017, and gives a quadratic mean of the albedo root-mean squared difference over the dual-view region which is comparable from month to month, with a 2017 average value of 0.01. Sources of discrepancies include the difficulty to estimate the flux over the sunglint ocean region close to the limbs, the fact that the data processing does not include dedicated angular distribution models for the aerosol-over-ocean case, and the existence of an observer-dependent diurnal-asymmetry artefact affecting the clear-sky-albedo dependence on the solar zenith angle particularly over land areas