Meteorological Applications最新文献

Droughts of various types are considered as a major threat to rainfed agriculture, because agricultural production is dependent on the spatio-temporal distribution of rainfall. The rainfed regions of Pakistan, such as Potohar, have experienced several instances of drought since 2000. The most drought-affected staple crop of the region is wheat, which ultimately affects regional food security. In this study, we examine the agricultural and the meteorological droughts in the Potohar region during 2000–2020, to create a drought risk map. This region comprises four districts, namely Attock, Chakwal, Jhelum and Rawalpindi. First, the agricultural and meteorological drought severity maps were created using rainfall data (meteorological data) and vegetation indices for three different periods (i.e., drought year, moderate drought year and normal year). The agricultural drought patterns and intensity were identified and evaluated using the MODIS products MOD09A1 and MOD11A2, while the meteorological droughts were identified using CHRIPS rainfall data. Afterwards, a combined drought risk map was generated by integrating the agricultural and meteorological adrought severity maps using a weighted overlay analysis. This drought risk map showed that Attock and Rawalpindi were expeiencing slight to no drought conditions, whereas the southwestern and central parts of Chakwal showed moderate drought conditions. Similarly, the western parts of Jhelum faced moderate drought conditions. Thus, the combined drought risk map may be a useful guide for decision makers in the local and provincial government. Using this map, they can identify adaptation practices in the drought-prone areas of this region for enhancing agricultural productivity.

Floods are among natural disasters which cause the largest damages worldwide each year, inducing fatalities of human lives, destruction of infrastructure and economical losses. Consequently, forecasting this type of events through hydro-meteorological models is still of great importance from a civil protection point of view since it allows to reduce hydrological risk by means of early warning systems. Nevertheless, hydrological model initialization in ungauged basins, where there is lack of direct measurements of meteorological information, is a known issue affecting the entire prediction chain. The present study evaluates the possibility of using forecasts provided by the meteorological model MOLOCH developed by CNR-ISAC forcing the FEST-WB hydrological model developed by Politecnico di Milano to perform discharge simulations assuming that the forecasting errors are negligible when using the first 24 h of time horizon. The study is carried out in the urban catchments of Milan city, the Seveso-Olona-Lambro (SOL) river basins, located in northern Italy. The main hydro-meteorological variables are analysed by comparing the spatialized and observed meteorological data, provided by an official regional network of weather stations plus a citizen scientists' contribution with the meteorological model forecasts. Moreover, a sensitivity analysis following the well-known one-factor-at-a-time methodology is accomplished with the aim of defining which atmospheric forcing, beyond rainfall, mostly affects flowrate forecasts. Results generally show satisfactory correspondences between forecasts and observed data for the discharge variable at daily scale, although an underestimation of precipitation, particularly for severe events in summer, is present. Therefore, using meteorological forecasts to create daily initial conditions for hydrological model, instead of ground observations, might be a reliable and valuable approach, even if some considerations should be borne in mind when coupling the two models.

Snowfall and wet snow accretion in the Kanto Plain, including the Tokyo Metropolitan Area, significantly impact the lives of people there. This study used a large ensemble simulation to predict future changes in the frequency of snowfall and wet snow accretion, and the accumulated snowfall and wet snow accretion during an event. We assessed wet snow accretion using a model that considers the effects of temperature, precipitation, wind speed, and humidity. The future snowfall event frequency and accumulated snowfall were predicted to decrease by 0.73 year⁻¹ (61%) and 0.69 mm (11%), respectively, under +2-K future climate projections from those under the present climate. 87% and 13% reduction in future snowfall events was due to increasing temperature and reduced frequency of extratropical cyclone passages, respectively. Moreover, the future frequency of wet snow accretion events and accumulated wet snow accretion are predicted to decrease by 0.84 year⁻¹ (90%) and 0.73 kg m⁻¹ (29%), respectively, from those in the present. 91% and 9% reduction in future wet snow accretion events was due to increasing temperature and reduced frequency of extratropical cyclone passages, respectively. Snowfall and wet snow accretion risk are predicted to decline under the +2-K future climate projections from those under the current climate. The risk can decrease more significantly in the coastal areas than in the inland areas. We hope that the information provided in this study will help policymakers of local governments in the Kanto Plain to implement appropriate measures against future snowfall and wet snow accretion.

Reserve power systems are widely used to provide power to critical infrastructure systems in the event of power outages. The reserve power system may be subject to regulation, typically focussing on a strict operational time commitment, but the energy involved in supplying reserve power may be highly variable. For example, if heating or cooling is involved, energy consumption may be strongly influenced by prevailing weather conditions and seasonality. Replacing legacy assets (often diesel generators) with modern technologies could offer potential benefits and services back to the wider electricity system when not in use, therefore supporting a transition to low-carbon energy networks. Drawing on the Great Britain telecommunications systems as an example, this paper demonstrates that meteorological reanalyses can be used to evaluate capacity requirements to maintain the regulated target of 5-days operational reserve. Across three case-study regions with diverse weather sensitivities, infrastructure with cooling-driven electricity demand is shown to increase energy consumption during summer, thus determining the overall capacity of the reserve required and the availability of ‘surplus’ capacity. Lower risk tolerance is shown to lead to a substantial cost increase in terms of capacity required but also enhanced opportunities for surplus capacity. The use of meteorological forecast information is shown to facilitate increased surplus capacity. Availability of surplus capacity is compared to a measure of supply–stress (demand-net-wind) on the wider energy network. For infrastructure with cooling-driven demand (typical of most UK telecommunication assets), it is shown that surplus availability peaks during periods of supply–stress, offering the greatest potential benefit to the national electricity grid.

A model-circulation increment-based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year-to-year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year-to-year increments of the summer rainfall anomaly and the associated simultaneous three-dimensional coupled air-sea circulation predictors. The cross-validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño-Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO.

Convincing commuters to use a bike is a timely contribution to reach sustainability goals. However, more than other modes of transportation, cycling is heavily influenced by the current meteorological conditions. In this study, we assess the weather conditions experienced on individual cycling routes through an urban environment and how weather observations and forecasts may give guidance to a better cycling experience. We introduce an agent-based model that simulates cycling trips in Hamburg, Germany, and a three-category traffic light scheme for precipitation, wind and temperature comfort. We use these tools to evaluate the cycling weather based on the commonly used single-station measurement approach versus spatially dense observations from an urban station network and radar measurements. Analysis of long-term data from a single station shows that most frequently discomfort is caused by temperature with a probability of 33%. Wind and precipitation discomfort occur only for about 5% of the rides. While temperature conditions can be well assessed by a single station, only one-third of critical precipitation events and less than 10% of critical wind events are captured. With perfect knowledge, temporal flexibility in start time of less than ±30 min reduces the risk of getting wet by 50%. For precipitation, nowcasting is able to predict 30% of the critical events correctly, which is significantly better than model forecasts. Operational ensemble forecast provides satisfactory guidance concerning temperature; however, the limited predictability of precipitation and wind renders these forecasts only useful for riders with a high risk-awareness and small sensitivity to false alarms.

The application of low-cost air quality monitoring networks has substantially grown over the last few years, following the technological advances in the production of cheap and portable air pollution sensors, thus potentially greatly increasing the limited spatial information on air quality conditions provided by traditional stations. However, the use of low-cost air quality sensors still presents many limitations, mostly related to the reliability of their measurements. Despite the increasing number of papers focusing on these issues, some of the challenges connected to the use of low-cost air quality sensors are still poorly investigated and understood, considering in particular those related to long-term applications of low-cost air quality networks and their integration within the reference air quality monitoring system. The present review aims at filling this gap, by analysing the characteristics of low-cost air quality monitoring networks that were run across long-term field campaigns, including their geographical location, the pollutants monitored, the type and number of stations employed, and the length of the field campaign, with a particular attention on assessing the aims for their deployment and on the evaluation of their integration within official air quality monitoring networks. Moreover, a critical analysis of the most insightful suggestions and recommendations delivered in the literature, as well as of the relevant critical issues, is presented, highlighting still open research areas and outlining future challenges.

Snowfall forecasting has historically been an area of difficulty for operational meteorologists, particularly in regions of complex terrain, such as the western United States. Attempts at improving forecasts have been made, but skill is still poor, with snowfall routinely overpredicted. A major reason for this overprediction has been the failure to accurately predict snow–liquid ratios (SLR) ahead of major events. This research proposes, develops, and tests multiple machine learning methods for dynamic SLR prediction for the Sky Islands of southeast Arizona by objectively comparing a multiple linear regression (MLR) against several more complex and flexible machine learning methods. Input parameters for each method were chosen based on variables found by previous studies to have a regression-based relationship with SLR, with a focus on the lower mid-levels of the troposphere. These parameters were also used to construct the MLR model, and its performance was compared objectively with the machine learning methods. When tested on historical events, a very high percentage of the network-predicted SLR values fall within the margin of error of observed SLRs, which were calculated using gridded snow depth and snow water equivalent (SWE) data from the University of Arizona daily 4-km SWE, SD, and SCE dataset (UASnow). A support vector machine (SVM), a k-nearest neighbor (KNN) algorithm, and a random forest also showed high accuracies when tested on the dataset, and each showed a significant gain in skill compared with the MLR model, with skill being evaluated by multiple metrics.

To be able to produce accurate and reliable predictions of visibility has crucial importance in aviation meteorology, as well as in water- and road transportation. Nowadays, several meteorological services provide ensemble forecasts of visibility; however, the skill and reliability of visibility predictions are far reduced compared with other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means estimation of the predictive distribution of the weather quantity at hand either by parametric or nonparametric approaches, including machine learning-based techniques. As visibility observations—according to the suggestion of the World Meteorological Organization—are usually reported in discrete values, the predictive distribution for this particular variable is a discrete probability law, hence calibration can be reduced to a classification problem. Based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts covering two slightly overlapping domains in Central and Western Europe and two different time periods, we investigate the predictive performance of locally, semi-locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers. We show that while climatological forecasts outperform the raw ensemble by a wide margin, post-processing results in further substantial improvement in forecast skill, and in general, POLR models are superior to their MLP counterparts.

The need to evaluate global climate data has increased in recent times. In this study, we evaluate the ability of global precipitation products to monitor drought during three wet seasons (Belg/March–May, Kiremt/June–September and Autumn/September–November) and associated rainfall regions in Ethiopia. We employed statistical methods to quantify and evaluate precipitation products based on probability of drought detection (POD), the extent of false alarms (FAR) and the critical success index (CSI) to see the overall performance of the studied precipitation products. The majority of the studied precipitation datasets were relatively better in capturing the Autumn drought in southern Ethiopia, and 18 out of 21 precipitation products captured accurately more than 50% of observed droughts. The CSI scores for this season are also above 0.5 for 14 precipitation products. On the other hand, 15 and 14 precipitation products accurately captured more than 50% of the seasonal drought in Kiremt and Belg rainfall seasons in north-eastern Ethiopia. In contrast, most precipitation products do not clearly represent the drought conditions of the Kiremt season in north-western Ethiopia. Only 8 of the 21 precipitation products accurately captured more than 50% of the observed drought in this region, and only 6 precipitation products had a CSI score greater than 0.5. The results can facilitate the selection of precipitation products for drought monitoring purposes, for use in specific wet seasons and regions of Ethiopia.