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  • Nowcasting - an overview | ScienceDirect Topics
    Nowcasting plays an increasingly important role in disaster risk management and risk prevention Given the utility of nowcasting, it is desirable that every National Meteorological and Hydrological Service (NMHS) develops its capacity to effectively render nowcasting services to the relevant users
  • Nowcasting Chinese GDP in a data-rich environment . . . - ScienceDirect
    To further reaffirm the effectiveness of ML learning algorithms in nowcasting China’s annualised real GDP growth rate, we provide two types of robustness checks by comparing the performance of the various models at forecasting annualised real GDP growth rate one-quarter-ahead and nowcasting the China’s quarter-on-quarter nominal GDP growth rate Table 4, Table 5 and Figure 7 in Appendix A
  • Nowcasting: The real-time informational content of macroeconomic data
    In nowcasting current-quarter GDP growth, qualitative judgment is typically combined with simple small-scale models that sometimes are called “bridge equations ” The idea is to use small models to “bridge” the information contained in one or a few key monthly data with the quarterly growth rate of GDP, which is released after the monthly data For example, see
  • U-RNN high-resolution spatiotemporal nowcasting of urban flooding
    Nowcasting plays an essential role in reducing economic cost and ensuring safety (Hallegatte, 2012) In highly urbanized catchments, flooding is strongly localized due to the presence of micro-scale urban features such as roads ( Logan and Reilly, 2023 ) and buildings ( Bates, 2023 ), rather than evolving over a wider area as in floodplains ( Fraehr et al , 2023 ) and river basins ( Najafi et
  • Nowcasting Chinese GDP in a data-rich environment . . . - ScienceDirect
    The remainder of the paper is organised as follows Section 2 reviews the literature related to nowcasting in a data-rich environment Section 3 describes our data Section 4 outlines our nowcasting methodology and algorithms, while Section 5 presents model estimation and nowcasting evaluation The empirical results are discussed in Section 6
  • Advances in nowcasting economic activity: The role of heterogeneous . . .
    Our model delivers a large and highly significant improvement relative to various model benchmarks: a basic DFM, a DFM extended to include time-varying long-run growth and stochastic volatility (Antolin-Diaz et al , 2017) and the nowcasting model maintained by the New York Fed until 2021 Both point and density forecasting improve statistically and economically Our model’s nowcasts are also
  • Solar radiation nowcasting based on geostationary satellite images and . . .
    Solar radiation nowcasting is a critical tool for PV power estimation and prediction While extrapolation-based methods fail to capture the dynamic and complex phenomena of the atmosphere, deep learning methods are starting to surpass their performance In this study, we used the DGMR architecture and adapted the model for use with MSG-4 global
  • Nowcasting with large Bayesian vector autoregressions
    Nowcasting – defined as the prediction of the present, the very near future, and the very recent past 2 – mimics, in an internally consistent and automated framework, the way in which markets process data releases in real-time It involves continuous and immediate updates of the predictions for a comprehensive measure of economic activity (usually, GDP, which is measured quarterly and
  • Precipitation nowcasting using transformer-based generative models and . . .
    Precipitation nowcasting based on U-Net model has previously demonstrated performance superior to that of a traditional radar-based precipitation nowcasting model that uses optical flow Based on it, we deploy some attention gates to U-Net networks as G to suppress irrelevant areas in the input image and highlight the salient features of specific local areas
  • Nowcasting: The real-time informational content of macroeconomic data
    In nowcasting current-quarter GDP growth, qualitative judgment is typically combined with simple small-scale models that sometimes are called “bridge equations ” The idea is to use small models to “bridge” the information contained in one or a few key monthly data with the quarterly growth rate of GDP, which is released after the monthly data For example, see Baffigi et al (2004





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