Data assimilation in weather forecasting is a well-known technique used to obtain an improved estimation of the current state of the atmosphere (analysis). The Meteorological Service of Catalunya (SMC) is seeking for ...
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Data assimilation in weather forecasting is a well-known technique used to obtain an improved estimation of the current state of the atmosphere (analysis). The Meteorological Service of Catalunya (SMC) is seeking for a real time high resolution analysis of surface parameters over Catalonia (north-east of Spain), in order to know the current weather conditions at any point of that region. For this purpose, a comparative study among several data assimilation experiments based on LAPS (Local Analysis and Prediction System) and STMAS (Space-Time Multiscale Analysis System) and multi-regression technique designed at SMC, has been performed to determine which one delivers best results. The comparison has been done using as true state independent observational data provided by the Spanish Meteorological State Agency (Agencia Estatal de METeorologia, AEMET). The results show that the multi-regression technique provides more accurate analyses of temperature and relative humidity than the LAPS/STMAS experiments, mainly due to the fact that multi-regression methodology only uses observations and consequently the model biases are avoided.
Assimilation of data into a fire-spread model is formulated as an optimization problem. The level set equation, which relates the fire arrival time and the rate of spread, is allowed to be satisfied only approximately...
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Augmented reality(onwards,AR)technologies are now much more complex and feature a higher number of details thanks to advancements in information and communication technologies(ICTs).Today’s systems can be easily adap...
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Augmented reality(onwards,AR)technologies are now much more complex and feature a higher number of details thanks to advancements in information and communication technologies(ICTs).Today’s systems can be easily adapted and packaged into smartphone apps which enable a wide range of applications in real and clinical *** impact on behavioral health treatments,rehabilitation and healthcare system in general is just beginning to become evident[1]but still,more research providing evidence-based practices is required.
In recent years, researchers have realized the difficulties of fitting power-law distributions properly. These difficulties are higher in Zipfian systems, due to the discreteness of the variables and to the existence ...
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In recent years, researchers have realized the difficulties of fitting power-law distributions properly. These difficulties are higher in Zipfian systems, due to the discreteness of the variables and to the existence of two representations for these systems, i.e., two versions depending on the random variable to fit: rank or size. The discreteness implies that a power law in one of the representations is not a power law in the other, and vice versa. We generate synthetic power laws in both representations and apply a state-of-the-art fitting method to each of the two random variables. The method (based on maximum likelihood plus a goodness-of-fit test) does not fit the whole distribution but the tail, understood as the part of a distribution above a cutoff that separates non-power-law behavior from power-law behavior. We find that, no matter which random variable is power-law distributed, using the rank as the random variable is problematic for fitting, in general (although it may work in some limit cases). One of the difficulties comes from recovering the “hidden” true ranks from the empirical ranks. On the contrary, the representation in terms of the distribution of sizes allows one to recover the true exponent (with some small bias when the underlying size distribution is a power law only asymptotically).
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