Meteorological Applications最新文献

The usage of weather apps for forecast information has increased dramatically over the last 10–15 years. Ensuring that consumers value and trust weather apps is important to the integrity of weather forecasting. Public perception of weather app forecast accuracy and consistency undergirds the apps' value and trustworthiness. With app forecasts being interpreted solely by the app user, misunderstanding and consequent false expectations could jeopardize the public's perception of accuracy and consistency. Furthermore, weather apps often offer excessively—and potentially unrealistically—detailed forecasts on time and spatial scales, extending far into the future without sufficient disclaimers regarding the confidence level associated with such detailed forecasts. A survey of the public found perceived app accuracy and consistency to be positively correlated with the trust in an app. Participants indicated that they take at least modest consideration of uncertainty and spatial variability when assessing specific and longer range forecasts. On average, participants had low to moderate confidence in forecasts beyond 10 days, and a significant majority did not perceive a precipitation forecast as inaccurate, even when no rain occurred at their location, as long as it rained nearby. We tested for misinterpretation using a common expression of uncertainty in weather apps, namely probability of precipitation (PoP). A majority of participants made a correct interpretation of the two PoP values given, although, depending on the percentage, some misinterpreted the values as indicating precipitation intensity, totals, or duration. Overall, these findings offer encouragement for a society heavily reliant on weather apps while also encouraging more research on weather information interpretation.

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias-correction methods are effective at removing systematic model biases, however the results are variable and season-dependent. Importantly, our results are based on fully independent cross-validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution-based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias-correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution-based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal.

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom-up energy consumption prediction and top-down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom-up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy-consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently.

The southwest Indian Ocean (SWIO) recently experienced its most active, costliest and deadliest cyclone season on record (2018–2019). The anticipation and forecasting of natural hazards, such as tropical cyclones, are crucial to preparing for their impacts, but it is important to understand how well forecasting systems can predict them. Despite the vulnerability of the SWIO to tropical cyclones, comparatively little research has focused on this region, including understanding the ability of numerical weather prediction systems to predict cyclones and their impacts in southeast Africa. In this study, we evaluate ensemble probabilistic and high-resolution deterministic forecasts of tropical cyclones in the SWIO from 2010 to 2020, using two state-of-the-art global forecasting systems: one from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the other from the U.K. Met Office. We evaluate predictions of the track, assessing the location of the centre of each storm and its speed of movement, as well as its intensity, looking at maximum wind speeds and minimum central pressure, and discuss how the forecasts have evolved over the 10-year period. Overall, ECMWF typically provides more accurate forecasts, but both systems tend to underestimate translation speed and intensity. We also investigate the impact of the Madden-Julian Oscillation (MJO) on tropical cyclones and their forecasts. The MJO impacts where and when tropical cyclones form, their tracks and intensities, which in turn impacts forecast skill. These results are intended to provide an increased understanding of the ability of global forecasting systems to predict tropical cyclones in the SWIO, for the purpose of decision making and anticipatory action.

A method for detecting clear-sky periods from photovoltaic (PV) power measurements is presented and validated. It uses five tests dealing with parameters characterizing the connections between the measured PV power and the corresponding clear-sky power. To estimate clear-sky PV power, a PV model has been designed using as inputs downwelling shortwave irradiance and its direct and diffuse components received at ground level under clear-sky conditions as well as reflectivity of the Earth's surface and extraterrestrial irradiance, altogether provided by the McClear service. In addition to McClear products, the PV model requires wind speed and temperature as inputs taken from ECMWF twentieth century reanalysis ERA5 products. The performance of the proposed method has been assessed and validated by visual inspection and compared to two well-known algorithms identifying clear-sky periods with broadband global and diffuse irradiance measurements on a horizontal surface. The assessment was carried out at two stations located in Finland offering collocated 1-min PV power and broadband irradiance measurements. Overall, total agreement ranges between 84% and 97% (depending on the season) in discriminating clear-sky and cloudy periods with respect to the two well-known algorithms serving as reference. The disagreement fluctuating between 6% and 15%, depending on the season, primarily occurs while the PV module temperature is adequately high and/or when the sun is close to the horizon with many more interactions between the radiation, the atmosphere and the ground surface.

China's severe particle pollution could affect the regional climate and weather conditions that consequently threaten local to global food security. Yet, the underlying mechanisms and quantitative assessment of aerosols on crop yields remain unknown. Here, by integrating a meteorology–chemistry model and a crop model, we show the impacts of atmospheric aerosols on China's meteorology and soybean yields. We find that the potential yields of soybean would decrease in most parts of China due to direct aerosol radiation effects, while showing diverse responses in parts of the Northeast and North China Plain. Moreover, because of the high sensitivity of soybean growth to water, potential yield fluctuations are closely related to aerosol-induced precipitation changes in most soybean-growing regions of China. In particular, aerosols play the most important role during soybean's pod-filling stage, in which the influence of both precipitation perturbations and negative solar radiative forcing is about 5–10 times that of air temperature on crop yield. Our study thereby identifies aerosol mitigation can bring a notable increase in crop yields, highlighting the potential for important co-benefits in food security across polluting developing countries.

In this article, a prediction model based on spatiotemporal stacked ResNet (Res-STS) for hourly temperature prediction is designed. On the timescale, the Res-STS removes the gate structure of the long short-term memory (LSTM) model, and the data of multiple consecutive time nodes are stacked together to preserve all temporal characteristics of the data. A point-to-point data mapping relationship is developed on the spatial scale to maximize the impact of large-scale environmental background field characteristics on a single grid point. Based on the historical gridded data from the China Meteorological Administration land data assimilation system (CLDAS) and the optimal factor dataset of the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) from 2017 to 2020, hourly temperature prediction models based on convolutional long short-term memory (ConvLSTM) and Res-STS model are developed, respectively. Furthermore, the prediction results of the two models in 2021 are compared with the ECMWF-IFS. The results show that the root mean square error (RMSE) of the prediction results by ConvLSTM and Res-STS models are both smaller than that of ECMWF-IFS. Specially, the Res-STS model performs best: it reduces the RMSE by 20.8% (24.5%) compared with the ConvLSTM (ECMWF-IFS). Specifically, the RMSE peaks in the afternoon when the daily maximum temperature occurs, while it is relatively smaller at night. Res-STS demonstrates a significant improvement in forecast performance compared with ECMWF-IFS, while ConvLSTM's correction during the period of maximum temperature occurrence has been enhanced. Moreover, the forecast performance of the Res-STS model is least affected by terrain compared with those of the ConvLSTM and ECMWF-IFS. For the regions with terrain height greater than 1 km, the model Res-STS evidently improves the RMSE.

Mesoscale convective systems (MCSs) are important air water sources to the Three-river-source (TRS) region known as the “Chinese water tower.” Using hourly equivalent blackbody temperature (T_BB) data from geostationary satellites of Chinese Fengyun-2 series during the warm season (May–August) in 2005–2020 and an objective algorithm, MCSs in the TRS are divided into meso-α (M_αCS), meso-β (M_βCS), and meso-γ (M_γCS), and M_αCS and M_βCS are subdivided into larger meso-α (LM_αCS), smaller meso-α (SM_αCS), larger meso-β (LM_βCS), and smaller meso-β (SM_βCS). Results show that a high-frequency zone of MCSs in the TRS distributes along the source of the rivers. Most MCSs, except LM_αCS, develop and dissipate in situ. The interannual variation in MCS frequency exhibits a decreasing trend, especially after 2013, mainly due to the decrease in MCSs in the source region of the Yellow–Lancang River. The occurrence of MCSs peaks in August, but MCSs are most likely to produce precipitation in July and usually generate between 1600–2200 h LST (UTC + 8). The precipitation caused by MCSs to the total precipitation (precipitation ratio, PR) accounts for about 40%; MCS PR is closely related to, and increases with, the horizontal scale of the MCS, with M_αCS PR being the highest, exceeding 67%. The contribution of MCSs to precipitation is mainly reflected in weak precipitation, smaller than 10.0 mm/h. Most of the maximum precipitation of MCSs appears after MCSs reach their prime, with the maximum lag by M_αCS up to 2 h.

Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi-decadal daily timeseries, they provide long-term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in-situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological-related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio-temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large-scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change).

An extreme haze-fog event occurred during October 20–22, 2013, in Harbin, Northeast China, which lasted for nearly 60 h with local visibility as low as 20 m. However, causes of the extreme haze-fog formation remain unclear. Through the analysis of in situ data and objective weather circulation classification, it is revealed that high pollutant emissions from biomass burning played a very important role in the extreme event. Stable weather conditions under the circulation type 8 (CT8), marked by weak high-pressure control, strong inversion (6.55°C), shallow boundary layer depth (<300 m), and high relative humidity (>90%), aided in the accumulation of pollutants and hygroscopic aerosol growth. All of these factors collectively contributed to the extreme haze-fog formation. The insights derived from this study can improve the predictability of extreme haze-fog events, and indicate that pollution emissions should be tightly controlled in the adverse meteorological circulation type in Northeast China.