Browsing by Subject "observations"

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  • Jylhä, Kirsti; Ruosteenoja, Kimmo; Räisänen, Jouni; Venäläinen, Ari; Tuomenvirta, Heikki; Ruokolainen, Leena; Saku, Seppo; Seitola, Teija (2010)
    Rapoetteja - Rapporter - Reports
  • Gregow, Erik (2018)
    Finnish Meteorological Institute Contributions 142
    Observations have been and are an important part of today's meteorological developments. Surface observations are very useful as they are, providing weather information for a point location. ough they do not give much information, if any, on what happens between the stations across a larger area. With models one can create an analysis of the meteorological situation, i.e. calculate and estimate what happens between these fixed observation points. Remote-sensing data, such as radar and satellite, are being processed and the output is given over a domain as an analysed product of their measurements. For example, radar gives a plot of where the rain is located, i.e. an analysis of the current precipitation. With a series of radar images, a human (subjectively) or a computer objectively) can process this information to estimate where the rain will move and be located within the next few minutes (even hours), i.e. a short forecast also called "nowcast". is applies to some extent also for other observations, such as satellite data (cloud propagation). But for most quantities (such as temperature, wind, etc) it is significantly harder to make such a nowcast, since these are influenced by many other factors and there is no linear development of them. Therefore, there are forecast models that solve physical and dynamic equations, so that one can estimate the future weather for the coming hours and days. A prerequisite for generating a forecast of high quality is to capture the initial weather conditions as best as possible. This is done using observations and they are introduced into the forecast model through different techniques, where the model creates its own analysis as the initial step. There remain problems since forecast models often are affected by physical disagreements, as the dynamic conditions are not in balance. This results in the model having a spin-up effect, where the meteorological quantities are not yet in balance with each other and the resulting weather conditions are not always reliable during the first hours. Hence, a lot of research is spent on how to reduce this spin-up effect and on the use of nowcast models, in order to deliver the best model results for the first few hours of the forecast period. In this dissertation, the research work has been to improve the meteorological analysis, algorithms and functionality, using the Local Analysis and Prediction System (LAPS) model. Different kinds of observations were used and their interdependencies have been studied, in order to combine and merge information from variousinstruments. Primarily focus has been to improve the estimation of precipitation accumulation and meteorological quantities that affect wind energy. The LAPS developments have been used for several end-users and nowcasting applications, and experimentally as initial conditions for forecast modelling. The studies have been concentrated on Finland and nearby sea areas, with the available datasets for this domain. By combining surface-station measurements, radar and lightning information, one can improve the precipitation-amount estimations. The use of lightning data further improves the estimates and gives the advantage of having additional data outside radar coverage, which can potentially be very useful for example over sea areas. In addition, the improved LAPS analyses (cloud-related quantities) and a newly developed model (LOWICE), calculating the electricity production during wintertime (taking into account the icing of wind turbine rotor blades which reduces efficiency), have shown good results.
  • Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Ballardini, M.; Banday, A. J.; Barreiro, R. B.; Bartolo, N.; Basak, S.; Benabed, K.; Bernard, J. -P.; Bersanelli, M.; Bielewicz, P.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Boulanger, F.; Bracco, A.; Burigana, C.; Calabrese, E.; Cardoso, J. -F.; Chiang, H. C.; Colombo, L. P. L.; Combet, C.; Comis, B.; Crill, B. P.; Curto, A.; Cuttaia, F.; Davis, R. J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J. -M.; Di Valentino, E.; Dickinson, C.; Diego, J. M.; Dore, O.; Douspis, M.; Ducout, A.; Dupac, X.; Dusini, S.; Keihänen, E.; Kurki-Suonio, H.; Lähteenmäki, A.; Savelainen, M.; Suur-Uski, A. -S.; Väliviita, J.; Planck Collaboration (2017)
    The characterization of the Galactic foregrounds has been shown to be the main obstacle in the challenging quest to detect primordial B-modes in the polarized microwave sky. We make use of the Planck-HFI 2015 data release at high frequencies to place new constraints on the properties of the polarized thermal dust emission at high Galactic latitudes. Here, we specifically study the spatial variability of the dust polarized spectral energy distribution (SED), and its potential impact on the determination of the tensor-to-scalar ratio, r. We use the correlation ratio of the CBB `angular power spectra between the 217 and 353 GHz channels as a tracer of these potential variations, computed on different high Galactic latitude regions, ranging from 80% to 20% of the sky. The new insight from Planck data is a departure of the correlation ratio from unity that cannot be attributed to a spurious decorrelation due to the cosmic microwave background, instrumental noise, or instrumental systematics. The effect is marginally detected on each region, but the statistical combination of all the regions gives more than 99% confidence for this variation in polarized dust properties. In addition, we show that the decorrelation increases when there is a decrease in the mean column density of the region of the sky being considered, and we propose a simple power-law empirical model for this dependence, which matches what is seen in the Planck data. We explore the effect that this measured decorrelation has on simulations of the BICEP2-Keck Array/Planck analysis and show that the 2015 constraints from these data still allow a decorrelation between the dust at 150 and 353 GHz that is compatible with our measured value. Finally, using simplified models, we show that either spatial variation of the dust SED or of the dust polarization angle are able to produce decorrelations between 217 and 353 GHz data similar to the values we observe in the data.
  • Vajda, Andrea; Tuomenvirta, Heikki; Jokinen, Pauli; Luomaranta, Anna; Makkonen, Lasse; Tikanmäki, Maria; Groenemeijer, Pieter; Saarikivi, Pirkko; Michaelides, Silas; Papadakis, Matheos; Tymvios, Filippos; Athanasatos, Spyros (Ilmatieteen laitos, 2011)
  • Lumme, E.; Kazachenko, M. D.; Fisher, G. H.; Welsch, B. T.; Pomoell, J.; Kilpua, E.K.J. (2019)
    We study how the input-data cadence affects the photospheric energy and helicity injection estimates in eruptive NOAA Active Region 11158. We sample the novel 2.25-minute vector magnetogram and Dopplergram data from the Helioseismic and Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory (SDO) spacecraft to create input datasets of variable cadences ranging from 2.25 minutes to 24 hours. We employ state-of-the-art data processing, velocity, and electric-field inversion methods for deriving estimates of the energy and helicity injections from these datasets. We find that the electric-field inversion methods that reproduce the observed magnetic-field evolution through the use of Faraday's law are more stable against variable cadence: the PDFI (PTD-Doppler-FLCT-Ideal, where PTD refers to Poloidal-Toroidal Decomposition, and FLCT to Fourier Local Correlation Tracking) electric-field inversion method produces consistent injection estimates for cadences from 2.25 minutes up to two hours, implying that the photospheric processes acting on time scales below two hours contribute little to the injections, or that they are below the sensitivity of the input data and the PDFI method. On other hand, the electric-field estimate derived from the output of DAVE4VM (Differential Affine Velocity Estimator for Vector Magnetograms), which does not fulfill Faraday's law exactly, produces significant variations in the energy and helicity injection estimates in the 2.25 minutes - two hours cadence range. We also present a third, novel DAVE4VM-based electric-field estimate, which corrects the poor inductivity of the raw DAVE4VM estimate. This method is less sensitive to the changes of cadence, but it still faces significant issues for the lowest of considered cadences (two hours). We find several potential problems in both PDFI- and DAVE4VM-based injection estimates and conclude that the quality of both should be surveyed further in controlled environments.