ECMWF short-term prediction accuracy improvement by deep learning.

This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main idea behind this work is to improve short-term (up to 3 days) forecasts delivered by a global numerical weather prediction (NWP) model called ECMWF (European Centre for Medium-Range Weather Forecasts). In comparison to the existing local weather models that typically provide weather forecasts for limited geographic areas (e.g., within one country but they are more accurate), ECMWF offers a prediction of the weather phenomena across the world. Another significant benefit of this global NWP model includes the fact, that by using it in several well-known online applications, forecasts are freely available while local models outputs are often paid. Our proposed ECMWF-enhancing model uses a combination of raw ECMWF data and additional input parameters we have identified as useful for ECMWF error estimation and its subsequent correction. The ground truth data used for the training phase of our model consists of real observations from weather stations located in 10 cities across two European countries. The results obtained from cross-validation indicate that our parametric model outperforms the accuracy of a standard ECMWF prediction and gets closer to the forecast precision of the local NWP models.

Bupivacaine hydrochloride complexation with some alpha- and beta-cyclodextrins studied by potentiometry with membrane electrodes.

Membrane electrodes selective to bupivacaine cations were developed and those with PVC-dibutylphthalate membrane containing sparingly soluble bupivacaine phosphotungstate appeared to be the most suitable. Inclusion complexation of bupivacaine cations with cyclodextrins was studied by potentiometric measurements of the free bupivacaine cation concentration in aqueous solutions of bupivacaine hydrochloride with cyclodextrin using the prepared electrodes. Native alpha-cyclodextrin (alpha-CD) and beta-cyclodextrin (beta-CD), as well as their random-substituted derivatives hydroxypropyl-alpha-cyclodextrin (HP-alpha-CD), hydroxypropyl-beta-cyclodextrin (HP-beta-CD) and methyl-beta-cyclodextrin (M-beta-CD), were chosen for the study. The measured potentiometric data processed both by a linear and nonlinear regression corroborated the formation of weak 1:1 bupivacaine cation-cyclodextrin complexes and the corresponding complexation constants K(11) approximately 50-155 M(-1) were evaluated by the non-linear least-squares method. The mutual order of K(11) values, especially alpha-CD > beta-CD, suggested that the bupivacaine butyl group was mainly responsible for the inclusion complexation; the highest K(11) was exhibited by M-beta-CD followed by alpha-CD. The observed complexation may substantially modify properties of bupivacaine hydrochloride dosage forms with sufficient concentration of cyclodextrin but bupivacaine cations are readily released from the weak cyclodextrin complexes by dilution.