Skillful medium-range weather forecasts are critical for water resources planning and management. This study aims to improve 15-day-ahead accumulated precipitation forecasts by combining biweekly weather and disaggregated climate forecasts. A combination scheme is developed to combine reforecasts from a numerical weather model and disaggregated climate forecasts from ECHAM4.5 for developing 15-...

Weather radar data quality control is extremely important for meteorological and hydrological applications. For weather radars, scatterers in the atmosphere are not only meteorological particles like cloud, rain drops, snowflakes, and hails, but also non-meteorological particles such as chaff, insects, and birds. For radar meteorologists it is a major issue and challenge to design numerical sch...

It is becoming increasingly important to be able to verify the spatial accuracy of precipitation forecasts, especially with the advent of high-resolution Numerical Weather Prediction (NWP) models. In this paper, the Fractions Skill Score (FSS) approach has been used to perform a scale-selective evaluation of precipitation forecasts during 2003 from the Met Office mesoscale model (12 km grid len...

We compare structural different methods of the artificial intelligence for wind power prediction modeling and build additionally ensembles of the models. As input variables for these prediction methods weather data of a numerical weather prediction model are used. The performance of the presented methods is compared to the predictions of the neural network based model.

Prediction limits are often attractive for carrying out multiple-comparisons-with-control hypothesis tests. We present algorithms for computing simultaneous confidence levels of partially sequential nonparametric prediction limit tests used in environmental monitoring. Algorithms are given for “pof-m” (m chances to get p observations “inbounds” at each of r locations to “pass”), “California” (e...

Abstract Atmospheric chemistry transport models have been extensively applied in aerosol forecasts over recent decades, whereas they are facing challenges from uncertainties emission rates, meteorological data, and over-simplified chemical parameterizations. Here, we developed a spatial-temporal deep learning framework, named PPN (Pollution-Predicting Net for PM 2.5 ), to accurately efficiently...

The forecast process within an operational weather centre is based upon the analysis, diagnosis and prognosis of skilled meteorologists. To aid their efforts, the forecasters use a wide range of tools including weather satellites, radar, workstations, and numerical weather prediction (NWP) models. Tremendous gains in NWP performance over the past 2 decades has made this tool typically the most ...

Using satellite-based multi-sensor observations, this study investigates Chl-a blooms induced by typhoons in the Northwest Pacific (NWP) and the South China Sea (SCS), and quantifies the blooms via wind-induced mixing and Ekman pumping parameters, as well as pre-typhoon mixed-layer depth (MLD). In the NWP, the Chl-a bloom is more correlated with the Ekman pumping than with the other two paramet...

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Police agencies have been collecting increasing amount of information to better understand patterns in criminal activity. Recently there is a new trend on using the data collected to predict where and when crime will occur. Crime prediction is greatly beneficial because if it is done accurately, police practitioner would be able to allocate resources to the geographic areas most at risk for cri...