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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). [ABSTRACT FROM AUTHOR]

Precipitation estimation models are typically sourced by rain gauges, weather radars and satellite observations. A relatively new technique of precipitation estimation relies on the network of Commercial Microwave Links (CMLs) employed for cellular communication networks: the rain‐inducted attenuation in the links enables the precipitation estimation. In the paper, it is analysed to what extent the precipitation derived from CML attenuation data is useful in estimation of the precipitation field with the high temporal and spatial resolution required in nowcasting models. Two methods of determination of precipitation along CMLs from attenuation of signal with several frequencies were proposed. Then, in order to generate precipitation field, three approaches for assigning appropriate precipitation values to a specific point or set of pixels along the link are developed and tested. The CML‐based estimates are compared with point observations from manual rain gauges and multi‐source precipitation fields using daily and half‐hourly accumulations. It was found that the CML‐based precipitation fields are much worse than radar‐derived estimates. At the same time, they had slightly poorer reliability than spatially interpolated telemetric rain gauge data and significantly higher reliability than satellite estimates. Furthermore, the impact of link characteristics, such as length and frequency, on the reliability of CML‐based precipitation estimates is analysed. [ABSTRACT FROM AUTHOR]

This paper aims to explore machine learning techniques for post‐processing high‐resolution Numerical Weather Prediction (NWP) products for the early detection of convection. Data from the Arome Ensemble Prediction System and satellite observations from the Rapidly Developing Thunderstorm (RDT) product by Météo‐France are used to train a recurrent neural network model to predict areas of total convection and moderate convection. The learning task is formulated as a binary classification problem using a long short‐term memory (LSTM) network architecture. Results from the LSTM model are compared with an object‐based probabilistic approach to forecast convection using metrics such as a receiver operating characteristics (ROC) curve, the Brier score and reliability. Results indicate that the LSTM model performs similarly to the object‐based probabilistic benchmark when classifying moderate convection areas and shows improved skill when classifying areas of total convective. Finally, the LSTM model results are presented within an air traffic management context to showcase the potential use of machine learning models within an operational application. [ABSTRACT FROM AUTHOR]

The article celebrates the 30th anniversary of the journal Meteorological Applications and provides a review of its accomplishments and historical context. The journal focuses on meteorological science, including weather and climate, and is aimed at forecasters, applied meteorologists, climate scientists, and users or providers of meteorological and climate services. Over the years, the journal has made changes to its submission and review processes, including moving to a free-format submission process, providing guidance on the use of color in figures, and implementing a double-blind review approach. The journal has seen an increase in the quality of submissions and its impact factor. Looking ahead, the journal plans to launch an Early Career Researcher Board and continue to publish papers on emerging technologies and issues related to climate change, renewable energy, geographic areas of focus, hydrology, economics, and the climate-water-food nexus. The success of the journal is attributed to the dedication and hard work of the editorial team, reviewers, and authors. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

Extreme high temperatures are associated with increased incidences of rail buckles. Climate change is predicted to alter the temperature profile in the United Kingdom with extreme high temperatures becoming an increasingly frequent occurrence. The result is that the number of buckles, and therefore delays, expected peryear will increase if the track is maintained to the current standard. This paper uses a combination of analogue techniques and a weather generator to quantify the increase in the number of buckles and rail related delays in the southeast of the United Kingdom. The paper concludes by assigning a cost to the resultant rise in delays and damage before making recommendations on how these effects can be mitigated. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper describes a new seamless blended multi‐model ensemble configuration of an existing probabilistic medium‐ to extended‐range weather pattern forecasting tool (called Decider) run operationally at the Met Office. In its initial configuration, the tool calculated and presented probabilistic weather pattern forecast information for five individual ensemble forecasting systems, which varied in terms of their number of ensemble members, horizontal resolution, update frequencies and forecast lead time. This resulted in multiple forecasts for the same validity time which varied in terms of forecast skill depending on the lead time in question. This presented challenges for end‐users (e.g., operational meteorologists) in terms of knowing which forecast output is best to use and at which lead time, as well as knowing what to do in situations where forecasts varied between ensembles. To get around these challenges, a new seamless blended multi‐model ensemble configuration has been implemented operationally, comprising of output from five separate ensembles, and provides a single best forecast from day one out to day 45. Objective verification for a set of eight weather pattern groups covering forecasts initialized over a 6‐year period (2017–2022) shows that the seamless blended multi‐model ensemble forecasts are at least as good as, if not better than the best performing individual model. [ABSTRACT FROM AUTHOR]

Lightning often causes death, injury, and damage to various facilities and equipment. Accurately detecting the spatial location of lightning occurrence by predicting thunderstorms and lightning is of great significance. Traditional lightning detection systems detect lightning by measuring the sound, light, and electromagnetic field information radiated by lightning. These methods typically have two problems. First, the detection process of lightning signals is susceptible to electromagnetic interference. Second, the equipment cost is high and is not friendly to some lightning detection tasks only targeted at specific scenarios. In order to detect lightning more conveniently, we propose a lightning detection model based on deep learning networks. With the increase in the use of cameras in modern society, designing lightning object detection networks based on deep learning is possible. However, two problems have been found in existing practice: (1) When strong lightning meteorological phenomena occur, the lightning features in the image are covered by bright electric lights, and convolutional neural networks cannot distinguish between strong lightning scenes and strong ultraviolet scenes. (2) The performance of convolutional neural networks is often related to the model's size. The larger the model, the stronger the performance of the network. However, in practical application scenarios, computing resources are insufficient to use sufficiently large networks. In this paper, we propose a simple and effective lightning object detection network (LD‐Net) and use a foreground‐background segmentation algorithm to locate frames containing lightning in the video. After using the knowledge distillation‐based model compression method, the mAP of the lightning object detection network with a backbone net of resnet with 18‐layer (LD‐Net‐18) can reach 82.4%. We hope that the proposed LD‐Net can serve as a simple and powerful alternative to traditional lightning detection methods, enhancing efficiency in lightning detection tasks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper analyzes rainfall data to characterize the rainy season and define rice and maize crop calendars for current and future conditions in Madagascar. The daily rainfall data are taken from observational climate records and climate model simulations from the CMIP6 under the SSP245 and SSP585 scenarios. Rainy season characteristics are calibrated to fit rice and maize crop growth stages. The comparison between the past (1950–2018) and the future (2030–2100) highlights changes in the onset and cessation dates, which happen later and earlier, respectively. This causes the reduction of the rainy season duration, which affects the rice and maize crop calendars, especially its sowing or seeding periods. The worst (best) case is mainly observed in the southeast (southwest). On the one hand, the southwestern region may need to adapt to grow rice and maize crops with short or medium crop cycles in the future. In the Highland or Central land, the length of the sowing or seeding period increases. On the other hand, the North and East face a significant reduction in the length of the sowing or seeding period. Rice endures more than maize. Growing rice crops twice a year may not be possible in the future. But rather, we observe minor changes in the West. Our analysis suggests the imperative necessity to advise smallholder farmers to rely on short crop cycle varieties of rice and maize crops. Predominantly, the harvesting period is postponed. It is recommended to carefully consider our results for the definition of food policies. [ABSTRACT FROM AUTHOR]

The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders. [ABSTRACT FROM AUTHOR]

This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

In this paper, we report on a wind observation method based on the movement of hot‐air balloons (HABs). A quality assessment was carried out by comparing against wind observations at the meteorological tower of Cabauw in the Netherlands during May–September 2018, and the obtained standard deviations in error were σu=0.65ms−1$$ {\sigma}_u=0.65\;{\mathrm{ms}}^{-1} $$ and σv=0.69ms−1$$ {\sigma}_v=0.69\;{\mathrm{ms}}^{-1} $$ for the measured zonal and meridional wind components, respectively. Subsequent comparison against short‐term forecasts of the HARMONIE‐AROME model showed a standard deviation of 2.5 ms−1 for the wind vector difference. From the HAB observation set, a case was selected with a rapidly changing wind field belonging to a small intensifying depression. The HAB wind observation was applied in data assimilation as a proof of principle for a single‐observation experiment. It is shown that in a complex baroclinic situation, the model state is slightly improved. [ABSTRACT FROM AUTHOR]

Water vapor plays an extremely important role in the monitoring and prediction of weather, and GNSS tomography can obtain 3D spatiotemporal change information and reliable water vapor profiles. In this paper, an improved global navigation satellite system (GNSS) tropospheric tomography technique using an ERA5 (the fifth generation ECMWF reanalysis) product is developed. First, the ERA5 product was adopted to analyze the spatiotemporal distribution of water vapor, and a water vapor density threshold defining the top of the tomography was determined; then, the height of the grid top (GT) of different seasons was obtained through linear fitting; finally, the water vapor value between GT and tropopause is constrained using the data of the ERA5 product as the initial value. The new method for using the ERA5 product to determine the height of the GT of the tomographic grid reduces the height of the top layer of the grid and increases the number of effective GNSS rays. Data from nine CORS stations in Hong Kong in 2019 were selected for experiments. The results showed that the new algorithm increased the number of effective satellite signals by 14%. In addition, the ERA5 data, the radiosonde data, and the COSMIC‐2 data were used as reference values. The accuracy of the water vapor density obtained by the algorithm was improved by 25%, 17% and 9%, respectively. [ABSTRACT FROM AUTHOR]

The frequency of flash floods resulting from heavy rainfall over West Africa has increased in recent years with serious socio‐economic consequences. Therefore, the need to utilize numerical weather prediction models to forecast heavy rainfall events reliably is also rising at many operational meteorological centres in West Africa. This paper evaluates the performance of the Consortium for Small‐scale Modelling (COSMO) model of the German Meteorological Services (DWD) in predicting rainfall over West Africa for high‐impact rainfall events that occurred between 19 and 26 August 2017. The paper aims to investigate the synoptic forcings modulating daily rainfall variability during that period. Results show that COSMO simulates adequately the spatio‐temporal variability of rainfall distribution over West Africa, though with inherent biases. COSMO displays a decreasing skill in producing spatial rainfall distribution as rainfall amounts tend to 30 mm and above. Additionally, areas of heavy rainfall, mostly about 100–300 km southwest of the core of the Africa Easterly Jet (AEJ), often coincide with areas of decreasing mean sea level pressure of at least 0.6 hPa and areas of increasing convective available potential energy of at least 500 J/kg. Although not in all cases, the trough of the Africa Easterly Wave (AEW) is always located to the east of these areas. We show that not every storm, especially east of the prime meridian, is associated with an AEW trough. COSMO is able to reproduce the atmospheric dynamics modulating the daily rainfall variability, in addition to capturing the daily propagation of the AEW trough, and the core of the AEJ. However, the reproducibility skill of the model in predicting atmospheric dynamics may not transform into the predictive skill of the model in producing rainfall. Nevertheless, operational forecasters may be able to determine likely areas of heavy rainfall by estimating the position of the AEJ core based on the position of areas of the least falling pressure from COSMO. Finally, the incorporation of the fractions skill score metric based on the neighbourhood approach could also assist operational forecasters to decide at which scale a severe weather alert can be issued. [ABSTRACT FROM AUTHOR]

As the penetration levels of renewable energy sources increase and climatic changes produce more and more extreme weather conditions, the uncertainty of weather and power production forecasts can no longer be ignored for grid operation and electricity market bidding. In order to support the energy industry in the integration of uncertainty forecasts into their business practices, this work describes an experiment conducted with 105 participants from the energy industry. In the framework of an IEA Wind Task 36 workshop, the experiment aimed to investigate existing psychological barriers in the industry to adopt probabilistic forecasts and to better understand human decision processes. We designed and ran a ‘decision game’ to demonstrate the potential benefits of uncertainty forecasts in a realistic—although simplified—problem, where an energy trader had to decide whether to trade 100% or 50% of the energy of an offshore wind park on a given day based on deterministic and probabilistic uncertainty day‐ahead forecasts. The focus thus was on a decision‐making process dealing with extremes that can cause high costs in the form of security issues in the electric grid for system operators, or high monetary losses for traders, who have bid a power production into the market that failed to be produced due to high‐speed shutdown of the wind turbines. This paper presents the obtained results, extracts behavioural conclusions and identifies how to overcome psychological barriers to the adoption of uncertainty forecasts in the energy industry.We designed a ‘decision game’ to demonstrate the potential benefits of uncertainty forecasts in a realistic—although simplified—problem, where an energy trader had to decide whether to trade 100% or 50% of the energy of an offshore wind park on a given day based on deterministic and probabilistic uncertainty day‐ahead forecasts. This paper presents the obtained results, extracts behavioural conclusions and identifies how to overcome psychological barriers to the adoption of uncertainty forecasts in the energy industry. [ABSTRACT FROM AUTHOR]

Extreme weather events can cause significant damage to power distribution network infrastructure, often resulting in power outages. Distribution Network Operators (DNOs) are faced with the challenging task of responding to these outages in real time while maintaining a resilient grid. Our paper presents an innovative approach to alert operators about the potential risk associated with upcoming extreme weather through a normalized fragility curve. The uniqueness of the curve is the ability to capture regional differences across a DNO's territory while presenting operators with a means of setting unified risk thresholds. This can support a proactive response and allow the staging of necessary resources to minimize the threat posed by such events. Our approach captures the changes in failure probability associated with differing wind regimes and demonstrates the benefit of sub‐regional meteorological information. The proposed approach is demonstrated for wind events using 20 years of historical fault records from a DNO in the United Kingdom (UK). While its efficacy is demonstrated for windstorms in the UK, the approach could be applied globally to develop normalized fragility curves for other types of seasonal extreme weather events such as snowstorms, hurricanes, or linked hazards such as wildfires. The approach can also facilitate an understanding of how infrastructure may operate under future climate conditions, supporting proactive adaptation. [ABSTRACT FROM AUTHOR]

The suggestion that variations in solar irradiance affect Earth's weather and climate is based on correlations between solar and meteorological parameters. Solar activity influences the Earth's magnetic field. The geomagnetic activity index (GAI) is one of the indexes that measure this influence. In this paper, we researched the link between GAI and the passage of atmospheric cold fronts (CFs) over the Belgrade region, Serbia. Three years of daily data for solar (coronal holes, protons velocity and density), geomagnetic (GAI, electrical potential of Earth's atmosphere) and meteorological (air temperature, wind and precipitation) parameters were analysed. We developed a methodology that established pairs of GAI peaks and CFs and found the time interval (TI) between them. In 88.5% of cases, we were able to pair a GAI and a CF, while in 11.44% pairing could not be established. The number of GAI peaks without assigned CFs increased as mean values of GAI decreased. Most frequently, following an increase in the GAI, a CF passed after 7 days (relative frequency 22.8%), followed by 6 days (19.9%) and 8 days (18.5%). These three TIs covered, in sum, 61.3% of cases. Mean TI value was 6.82 days. The warm part of the year had the same TI distribution but the cold part of the year produced a bimodal frequency, with a maximum of 8 days (21.6%), followed by 5 (18.3%) and 6 days (17.6%). The most frequent CFs passing over Belgrade are from WNW‐N (67.1%) and SSW‐W (27.6%) quadrants. [ABSTRACT FROM AUTHOR]

Aeronautical Meteorological Offices under National Meteorological and Hydrological Service provide critical meteorological, hydrological, ocean and climatological information that sustain air navigation safety, efficiency, and regularity. Thus, to quality assure the information, the International Civil Aviation Organization recommended in 2002 that Aeronautical Meteorological Offices should implement ISO 9001 quality management system, which was subsequently standardized and became effective on 15 November 2012. There has been a slow movement towards adoption due to a number of barriers. In July 2019, 52% of Aeronautical Meteorological Offices commenced transition to the fifth iteration, ISO 9001:2015. A range of studies have investigated the barriers to successful quality management system implementation and certification within various organizations. However, only one study examined the Aeronautical Meteorological Offices and none of them covered the Anglophone Caribbean. Hence, the demography of the sample for this study is unique and this paper will contribute to filling the gaps in the literature. This exploratory study aims to identify the barriers, investigate their impacts, and propose recommendations to assist the Aeronautical Meteorological Offices to fully implement the ISO standard. Trinidad and Tobago Meteorological Service was selected as a case study due to its progress in the quality management system implementation and the scope of its operations. The study used and triangulated data collected from secondary sources (desktop research) and primary sources (survey and interviews). Seventeen barriers were identified and formed the new empirical framework for the Aeronautical Meteorological Offices in the Anglophone Caribbean. Most barriers were in the category of resources. The findings have significant implications for the policymakers, especially executive management, to address the barriers that are risks to sustainable quality management system. [ABSTRACT FROM AUTHOR]

Extreme rain events have caused numerous issues and have had a significant impact on agriculture, human activities, ecology, infrastructure, and casualties. The theory of extreme values has been widely applied in extreme precipitation modelling and a variety of other fields. This paper employs the generalized Pareto distribution, which has been widely used to analyse extreme climates, in conjunction with the peak over thresholds approach to investigate exceedances. The occurrence of intense rainfall events in Kigali city causes severe damage to human properties, infrastructure damage, people injuries, loss of life, and other various harmful consequences. Early detection of extreme rainfall in Kigali aids in the development and implementation of strategies and measures to mitigate the negative effects of extreme rainfall before it occurs. The aim of this research is to estimate the frequency and magnitude of intense rainfall events in Kigali. The daily rainfall data from Kigali Airport station collected by Rwanda Meteorological Agency from 1990 to 2019 were applied. The results showed that as the return periods increased, so did the return levels, implying that the intensity and frequency of rainfall in Kigali will increase in the future. The model's goodness was tested, and the study suggests a model that has a non‐negative shape parameter (ξ) to be good. The study's findings are extremely important for understanding the occurrence of these events and also serve as a tool for decision‐making and the development of policies aimed at mitigating the effects of such events. [ABSTRACT FROM AUTHOR]

Crowdsourced vehicle-based observations have the potential to improve forecast skill in convection-permitting numerical weather prediction (NWP). The aim of this paper is to explore the characteristics of vehicle-based observations of air temperature in the context of data assimilation. We describe a novel low-precision vehicle-based observation dataset obtained from a Met Office proof-of- concept trial. In this trial, observations of air temperature were obtained from built-in vehicle air-temperature sensors, broadcast to an application on the participant's smartphone, and uploaded, with relevant metadata, to the Met Office servers. We discuss the instrument and representation uncertainties associated with vehicle-based observations and present a new quality-control procedure. It is shown that, for some observations, location metadata may be inaccurate due to unsuitable smartphone application settings. The characteristics of the data that passed quality control are examined through comparison with United Kingdom variable-resolution model data, roadside weather information station observations, and Met Office integrated data archive system observations. Our results show that the uncertainty associated with vehicle-based observation-minus-model comparisons is likely to be weather-dependent and possibly vehicle-dependent. Despite the low precision of the data, vehicle-based observations of air temperature could be a useful source of spatially-dense and temporally-frequent observations for NWP. [ABSTRACT FROM AUTHOR]

This paper analyses over 10,000 quality‐controlled pilot reports of low‐level wind shear ('wind shear') at the Hong Kong International Airport (HKIA; ICAO code: VHHH) between 2001 and 2019. HKIA is well known for its susceptibility to wind shear, which is a potential hazard for aircraft during landing and take‐off. Wind shear at HKIA exhibits strong seasonality with double peaks in the middle of the spring and summer months. There is a strong diurnal cycle in report numbers, peaking towards the afternoon, as modulated by air traffic at HKIA. On average, there are 115 days per year with wind shear reported, with possible power law distribution in the number of days with different number of reports. By comparing the background wind distribution during those minutes with pilot reports against the climatological distribution, wind shear is observed to be, on average, favoured under higher wind speeds (≥6 m s−1) and reduced under lower speeds with shifted frequency peak and heavier tail, although statistical behaviour can differ significantly across individual runway corridors. Preferred background wind directions for wind shear occurrence highlight the role of terrain influence upstream of HKIA. While positive shear (65.6%) is more often reported than negative shear (34.4%), both types of wind shear events appear to follow an exponential distribution in shear magnitude, with similar rates of decay. This is by far the largest statistical study of low‐level wind shear at an airport. Results can serve as an important local reference in addition to being a useful example for airports around the world. [ABSTRACT FROM AUTHOR]

This paper presents a new method to predict global solar radiation over irregular terrain, named Estimation of global solar RADiation (ERAD). The method is based on the disaggregation of Satellite Applications Facility on Land Surface Analysis (LSA SAF) data using a digital elevation model and is applied in Italy with a time step of 1 min and a spatial resolution of 200 m. A quantitative assessment of ERAD is performed in comparison with three other standard methods (Mountain Microclimate Simulation Model [MTCLIM], LSA SAF and Copernicus Atmosphere Monitoring Service [CAMS]) using measurements taken in 43 stations located in Italy or in the surrounding countries, in the years 2005–2016. Such assessment concerns the irradiance incoming on a horizontal surface, which is measured by ground radiation sensors and is summarized by means of four accuracy statistics (i.e. mean absolute error [MAE], root mean square error [RMSE], coefficient of determination [R2] and mean bias error [MBE]). Overall, the average daily global solar radiation estimates obtained by ERAD have RMSE and R2 about 25 W·m−2 and 0.943, respectively. These statistics are similar to those of LSA SAF and better than those of CAMS and, above all, MTCLIM. The bias analysis by elevation ranges shows a slight ERAD overestimation over plains and hills and a slight underestimation over mountains. An additional qualitative assessment shows how the ERAD radiation estimates are more spatially detailed than those of the other methods and are redistributed on inclined surfaces consistently with expectations.This paper presents a new method to predict global solar radiation over irregular terrain, named Estimation of global solar RADiation (ERAD). Solar radiation is downscaled over Italy using a digital elevation model at 200 m and a time step of 1 min. ERAD is assessed in comparison with three other standard methods (MTCLIM, LSA SAF and CAMS) versus observations taken in 43 weather stations from 2005 to 2016. The daily global solar radiation estimates obtained by ERAD have root mean square error and R2 about 25 W·m−2 and 0.943, respectively. [ABSTRACT FROM AUTHOR]

Intensity prediction of tropical cyclones (TC) has been one of the major challenges for the operational forecast and warning service, as well as consequential assessment of impacts including high winds, storm surge and heavy rainfall caused by TC. With the advances in global numerical weather prediction (NWP) modelling systems, TC track and intensity forecasts for medium range are available every 6 or 12 h, and ensemble prediction system (EPS) outputs provide various scenarios for producing probabilistic forecasts. The TC intensity forecast from the EPS of the European Centre for Medium‐Range Weather Forecasts (ECMWF) has shown systematic negative biases, although the performance is better than other global models in general. A machine learning model based on XGBoost, a decision‐tree‐based machine learning algorithm, is introduced in this paper to post‐process ECMWF EPS outputs and generate an improved forecast of TC intensity. The predictors such as selected percentiles of ensemble members in maximum wind and minimum pressure of previous TC cases were applied in the XGBoost model to generate a calibrated forecast for the maximum wind of TCs. Verification of the XGBoost model was made using TCs over the western North Pacific during 2016–2019. It is found that the negative biases of the intensity forecast from ECMWF EPS and HRES can be reduced with improvement in the overall accuracy. [ABSTRACT FROM AUTHOR]

Extreme rainfall events have posed several serious threats to many populated and urbanized areas in the world including the Indian subcontinent. Therefore, accurate predictions of their intensity and areas of influence are important for flood‐prone risk assessments. On 14 July 2016, heavy to exceptionally heavy rainfall occurred in Yamunanagar (30.16° N, 77.29° E), located in the state of Haryana in North India, which led to widespread disruption of communication, electricity, inundation of houses, and so forth. The present paper aims at examining observational, synoptic, thermo‐dynamical, and numerical features associated with this devastating rainfall episode. The analysis found that during extreme rainfall episodes, a trough in mid‐tropospheric westerlies and a strong low‐level atmospheric monsoonal flow seem to have strongly interacted with each other, creating a strong convergence zone near study areas that led to a severe rainstorm. The quasi‐stationary supercells were also noticed due to continuous moisture incursions from the Bay of Bengal and orographic uplift over the Himalayas near Yamunanagar. A deep layer of wind shear interacts dynamically with the convergence zone and leads to a potential rainstorm. Thermodynamic indices indicate high instability over the heavy rainfall area. The dynamics of this event were studied in detail by using three‐dimensional variational data assimilation within the weather research and forecasting model, configured with triple two‐way nesting domains (27, 9, and 3 km). The model results show that the weather research and forecasting model satisfactorily captures the quantitative precipitation (300 mm) in 24 h over the Yamunanagar region as compared with observation (365 mm). [ABSTRACT FROM AUTHOR]

In this paper, a data‐driven bias correction approach based on deep learning is proposed, which is appropriate for the Yin–He global spectral model (YHGSM) re‐forecasting. The proposed architecture involves four U‐Net‐based networks estimating the proper bias correction models for YHGSM re‐forecasting that consider as correction factors the geopotential, specific humidity, and vertical velocity on three pressure levels from the YHGSM model. The proposed models are then evaluated for their bias correction capability on the 3‐h cumulative precipitation over the region of China between 15°–54.5° N, and 63°–122.5° E. The results revealed that U‐Net‐based models could reduce the root mean squared error (RMSE) and improve the threat scores (TSs), especially for heavy precipitation and rainstorms.The architecture of U‐Net based model. When the red arrow indicates double convolution, the original U‐Net and Att‐UNet are indicated, respectively, according to whether the attention module is used or not. When the red arrow indicates the residual, it indicates Res‐UNet, and if using the attention module then it is RA‐UNet. [ABSTRACT FROM AUTHOR]

National meteorological and hydrological services (NMHSs) play a key role in gathering data and in providing services like early warnings, basic weather forecasts or climate analysis. In most developing countries, NMHSs are public services facing hindering financial constraints and shortages of skilled personnel. Both the World Meteorological Organization (WMO) and academia play a long-standing role in creating, innovating and fostering technology and services in meteorology and hydrology (hydromet services). At the same time, the role of the private sector in weather service provision is strongly growing all around the world. In order to maximize socio-economic benefits from hydromet services, it is necessary that countries are considering how to strategically embrace the benefits that the private and academic sectors can offer without jeopardizing the provision of public services. A recent World Bank report suggests that public–private engagement (PPE) can create and sustain effective hydromet value chains. Moving towards PPE not only creates opportunities for the academic sector but in many instances critically depends on it for capacity building, to further develop trust relationships between the sectors, to provide spaces in which collaboration between the sectors can be tested and last but not least serve as a source and conduit for new technologies. This paper intends to stimulate discussion on PPE and the role that academics and academic institutions can take to foster sustainable hydromet value chains. [ABSTRACT FROM AUTHOR]

This paper describes a novel scheme for assessing the ability of a numerical weather prediction (NWP) model to forecast the horizontal spatial structure of local extremes, for example, when comparing accumulated precipitation to a corresponding observed field. Both local maxima and minima are considered. The quality is measured by a score function between 0 and 1, which could potentially be adjusted for specific applications of NWP. The effect of neighbourhood size is included in order to consider the impact of resolvable scales. The scheme complements existing spatial verification schemes with added value and has a potential to be used both in operational verification and as a tool for revealing model deficiencies linked to individual forecast cases. The properties of the scheme are illustrated from various idealized cases. Verification results are presented for a real convective precipitation case and for a frontal precipitation case over several days. The scheme is discussed in the context of operational conditions, using synthetic fields of forecasts and analyses based on statistics of an operational NWP model at the Danish Meteorological Institute (DMI) and related observed data from a 5‐year period. Different future options are outlined, including the potential application of the scheme for ensemble prediction systems.Verifying the ability of numerical weather prediction to forecast extremes of precipitation accurately in space and amount is challenging and important. A verification scheme is presented that addresses this issue. The scheme may be used both on individual cases and in the context of operational model statistics. [ABSTRACT FROM AUTHOR]

Non‐catching type gauges are the emerging class of in situ precipitation measurement instruments. For these instruments, rigorous testing and calibration are more challenging than for traditional gauges. Hydrometeors characteristics like particle size, shape, fall velocity and density must be reproduced in a controlled environment to provide the reference precipitation, instead of the equivalent water flow used for catching‐type gauges. They are generally calibrated by the manufacturers using internal procedures developed for the specific technology employed. No agreed methodology exists, and the adopted procedures are rarely traceable to internationally recognized standards. The EURAMET project 18NRM03 'INCIPIT' on 'Calibration and accuracy of non‐catching instruments to measure liquid/solid atmospheric precipitation', funded by the European Metrology Programme for Innovation and Research (EMPIR), was initiated in 2019 to investigate calibration and accuracy issues of non‐catching measuring instruments used for liquid/solid atmospheric precipitation measurement. A survey of the existing models of non‐catching type instruments was initially performed and this paper provides an overview and a description of their working principles and the adopted calibration procedures. Both literature works and technical manuals disclosed by manufacturers are summarized and discussed, while current limitations and metrological requirements are identified. [ABSTRACT FROM AUTHOR]

Aircraft play an important role in providing in‐situ upper air observations for use in numerical weather prediction (NWP) models. Aircraft observations of wind, temperature and humidity have been routinely assimilated into NWP models for decades. More recently, the Met Office has installed a network of six receivers to collect Mode‐Select Enhanced Surveillance (Mode‐S EHS) broadcasts and derive wind and temperature information. The paper focuses on the assimilation of high‐frequency (sub‐hourly) Mode‐S winds into the Met Office's hourly cycling high‐resolution NWP model, which employs four‐dimensional‐variational data assimilation (4D‐Var). An observing system experiment (OSE) experiment was carried out to assess the impact of additionally assimilating Mode‐S wind data on the model forecasts in both summer and winter. It is shown that assimilating Mode‐S winds has a positive benefit on the forecast skill in the first 6 hr wind profiles, with an improvement of the mean bias and root mean square (RMS) errors of up to 0.5 m·s−1 in the first hour and 0.3 m·s−1 in the sixth hour. The assimilation of Mode‐S winds is also seen to be beneficial to the near‐surface‐focused UK NWP composite index, and to the hourly precipitation accumulations in fractions skill score (FSS) for forecasts up to 9 hr ahead. [ABSTRACT FROM AUTHOR]

The Beaufort Scale was used to estimate wind speeds (WSs) over oceans before the introduction of ship‐mounted anemometers. Beaufort‐estimated WSs form a major component of most historical data sets such as those of the International Comprehensive Ocean—Atmosphere Dataset (ICOADS) and data obtained from the Indian Meteorological Department (IMD). The Beaufort‐estimated WSs in the individual records of the ICOADS and IMD data are based on the World Meteorological Organization (WMO) 1100 scale, which gives biased WS equivalents. Corrections to this scale have been formulated to give better estimates of the WS. Among others, the corrections proposed by Da Silva and Lindau are found to give least bias. However, the correction scale derived by Da Silva has limited utility over the Indian Ocean. The present paper derives a correction explicitly for the Tropical Indian Ocean (30–100 ° E, 30 ° S–30 ° N) using individual records from the ICOADS and IMD unique data for the period 1985–2005. There is significant improvement in the agreement between Beaufort estimates and the anemometer‐measured WSs based on these corrections. The regression co‐efficients for the new scale are derived using annual and monthly data. The correction scale derived, which is based on the regression co‐efficients for July (July scale), gives less bias compared with the scales of other months. The slope differs by almost 17% when the new July scale is applied to the annual data when compared with the Da Silva scale; the bias showed a reduction from 0.52 to 0.08 m/s. The present paper derives a correction explicitly for the Tropical Indian Ocean (TIO) (30–100 ° E, 30 ° S–30 ° N) using individual records from the International Comprehensive Ocean—Atmosphere Dataset (ICOADS) and Indian Meteorological Department (IMD) unique data for the period 1985–2005 to correct the Beaufort‐estimated wind speeds (WSs) observed over the TIO. There is significant reduction in bias between Beaufort estimates and the anemometer‐measured WS after the application of the newly derived correction. [ABSTRACT FROM AUTHOR]

The Met Office Global Hazard Map (GHM) summarizes the risk of high‐impact weather across the globe over the coming week using global ensemble forecast data. In addition to gridded daily probability forecasts, a symbol and polygon‐based summary layer gives an at‐a‐glance view of likely high‐impact weather for the week ahead. To evaluate the performance of the GHM, two complementary approaches were used. The first is an objective precipitation verification approach comparing the daily gridded precipitation forecasts with global precipitation observations. The second, and the main focus of this paper is a new, semi‐automated evaluation approach that assesses the ability of the multi‐model ensemble precipitation summary layer to highlight events that cause community impacts, as recorded in an impact database. The verification against observed precipitation confirms there is good skill in the precipitation forecasts and that the multi‐model ensemble provides the best guidance to take forward into the summary layer. The verification against impacts indicates there is a good spatial relationship between the GHM precipitation forecasts and heavy rainfall impacts across the globe. Hit rates for all impact severities range from 40 to 60% for days 1–3 and taper off to lower hit rates at the longer lead times (10–20% for days 6–7). High‐impact events are captured marginally less well than the low‐, medium‐ and disastrous‐impact event categories and this paper illustrates a number of approaches that could positively alter the profile of the hit‐rate curve. The Met Office Global Hazard Map (GHM) summarizes the risk of high‐impact weather across the globe over the coming week using global‐ensemble forecast data. A new, semi‐automated evaluation approach that assesses the ability of the multi‐model ensemble precipitation summary layers to highlight events that cause community impacts, as recorded in an impact database, is described. Hit rates for all impact severities range from 40 to 60% for days 1–3 and taper off to lower hit rates at the longer lead times (10–20% for days 6–7). [ABSTRACT FROM AUTHOR]

The main approach to probabilistic weather forecasting has been the use of ensemble forecasting. In ensemble forecasting, the probability information is generally derived by using several numerical model runs, with perturbation of the initial conditions, physical schemes or dynamic core of the numerical weather prediction (NWP) models. However, ensemble forecasting usually tends to be under-dispersive. Statistical post-processing has, therefore, become an essential component of any ensemble prediction system aiming to improve the quality of numerical weather forecasts as they seek to generate calibrated and sharp predictive distributions of future weather quantities. Different versions of the ensemble model output statistics (EMOS) and the Bayesian model averaging (BMA) post-processing methods are used in the present paper to calibrate 24, 48 and 72 hr forecasts of 24 hr accumulative precipitation. The ensemble employs the weather and research forecasting (WRF) model with eight different configurations which were run over Iran for six months (September 2015-February 2016). The results reveal that the BMA and EMOScensored, shifted gamma (CSG) techniques are substantially successful at improving the raw WRF ensemble forecasts; however, each approach improves different aspects of the forecast quality. The BMA method is more accurate, skilful and reliable than the EMOS-CSG method, but has poorer discrimination. Moreover, it has better resolution in predicting the probability of highprecipitation events than the EMOS-CSG method. [ABSTRACT FROM AUTHOR]

A weak waterspout was observed at the Hong Kong International Airport on the morning of June 8, 2020. This paper documents the meteorological observations of this weak system, which was well captured by surface wind sensors and a Doppler weather radar, and this study also investigated the capability of a high-resolution numerical weather prediction model to simulate the system in a near real-time configuration. Despite the short-lived nature of the vortex, the model effectively reproduced the occurrence of a weak vortex around the time the system was observed, although with positional errors of the order of a few kilometres. The simulated vertical cross-sections of the reflectivity and Doppler velocity were consistent with the actual radar observations and reflected the slanting nature of the micro-vortex. These results may serve as a useful reference for the study of weak waterspouts worldwide. [ABSTRACT FROM AUTHOR]

In climate change research, accurate temperature data are often demanded. However, affected by many factors, especially solar radiation, the accuracy of environmental air temperature measurement can be greatly reduced, since there is a difference in temperature between the environmental air and the related temperature measured by the sensor accommodated inside the radiation shield. In the paper, the term “temperature error” refers to the temperature difference described above. To improve the accuracy of the temperature data, a temperature errorcorrection method is proposed. First, a computational fluid dynamics (CFD) method is adopted to quantify the temperature errors accurately. A neural network algorithm is then applied to form a universal correction equation by fitting temperature errors calculated using the CFD method. Finally, to validate the correction equation, field observation experiments are performed. The root mean square error (RMSE) and the mean absolute error (MAE) between the temperature errors obtained experimentally using a sensor inside the DTR503A shield and the corresponding temperature errors determined by using the proposed correction method are 0.043 and 0.038°C, respectively. The RMSE and MAE for the DTR13 radiation shield are 0.049 and 0.044°C, respectively. This method may reduce the error of the temperature data to 0.05°C. If the environmental factors corresponding to the temperature data can be quantified accurately, the factors influencing the temperature error can be added to the correction method continuously. The accuracy of this correction method may be furtherly improved. [ABSTRACT FROM AUTHOR]

In the real world, precipitation data of automatic weather stations are easily influenced by direct thunderstrokes, instrument ageing, electromagnetic interference, human operation errors and other factors. When close to the observation time, if the missing automatic station data cannot be corrected in a timely fashion, the whole quality of the station data will be affected. Thus, correct handling of the missing precipitation data to maintain their integrity has important significance. In this paper, we propose a “from coarse to fine” (FCTF) neural network to fill out the missing blanks and experiments show that this method to solve the problem of meteorological data shortage is effective. [ABSTRACT FROM AUTHOR]

Reliable information on the likelihood of drought is of crucial importance in agricultural planning and humanitarian decision‐making. Acting based upon probabilistic forecasts of drought, rather than responding to prevailing drought conditions, has the potential to save lives, livelihoods and resources, but is accompanied by the risk of acting in vain. The suitability of a novel forecasting tool is assessed in the present paper in terms of its ability to provide skilful information of the likelihood of drought impacts on crops and pasture within a timeframe that allows for anticipatory action. The Tropical Applications of Meteorology using SATellite data—AgriculturaL Early waRning sysTem (TAMSAT‐ALERT) tool provides forecasts of seasonal mean soil moisture and the water requirement satisfaction index (WRSI). TAMSAT‐ALERT metrics were found to be strongly correlated with pasture availability and maize yield in Kenya and provided skilful forecasts early in key seasons, allowing sufficient time for preparatory actions. Incorporating TAMSAT‐ALERT forecasts in a layered approach, with actions triggered by spatiotemporally varying triggers and fundamentally informed by humanitarian actors, will provide reliable information on the likelihood of drought, ultimately mitigating food insecurity.Anticipatory drought‐risk management is gaining considerable attention owing to its potential to improve food security. The suitability of Tropical Applications of Meteorology using SATellite data—AgriculturaL Early waRning sysTem (TAMSAT‐ALERT) soil moisture and water requirement satisfaction index (WRSI) forecasts are assessed in terms of their ability to provide skilful information on the likelihood of drought impacts within a timeframe that allows for anticipatory action. Triggers based on TAMSAT‐ALERT forecasts and developed in collaboration with humanitarian actors present promise for use in anticipatory drought‐risk management. [ABSTRACT FROM AUTHOR]

Small-scale, fast-evolving airflow disturbances, possibly due to upstream building clusters, are known to affect aircraft landing at corridor 25RA of Hong Kong International Airport (HKIA), China, under southerly or southwesterly winds. The paper presents a new algorithm, based on the concept of coherent integrated differential velocity (CIDV), for the automatic alerting of such hectometric (scale of hundreds of metres) turbulence features using short-range LIDAR (SRL) scans at 30 m radial resolution and an observation frequency of around every 20 s by Hong Kong Observatory (HKO). The CIDV identifies the cumulative effect of coherent patches of radial velocity changes occurring along the landing flight path by comparing two consecutive subminute SRL scans. Verification was conducted between June and September 2017 against a selection of relevant pilot reports, with a positive skill level observed over the whole range of the CIDV thresholds considered. When simulating a realistic setting where the SRL would issue alerts in conjunction with HKO's operational Windshear and Turbulence Warning System (WTWS), the CIDV algorithm would bring a gain in hit rate between 5% and 25%, with a corresponding increase in alert duration between 0.4% and 20%. The chosen CIDV threshold of 0.4 m·s-1 nautical mile (nm) can be physically interpreted as an average radial velocity change of 4.0 m·s-1 sustained over a distance of < 200 m (or between 2 and 3 s of flight), which agrees with reported values of crosswind changes considered significant for a landing aircraft and also pilots' perception of the characteristics of such events. [ABSTRACT FROM AUTHOR]

Over recent years, several visualization tools that offer advanced threedimensional (3D) rendering techniques, while still showing good performance for large data sets, have been developed. However, how to provide an intuitive way to combine traditional two-dimensional (2D) methods essential in operational weather forecasting and new 3D techniques welcomed in atmospheric research is not fully investigated, if not overlooked, in these tools. The paper presents MeteoExplorer: a software tool designed to analyse and visualize atmospheric and geoscience data. It consists of a developer-orientedcrossplatform foundation framework and several applications built upon the framework. The framework is developed with C++ on the central processing unit side and shader languages on the graphics processing unit (GPU) side. Modules in the framework are loosely coupled with minimum interdependencies to facilitate code reuse and improve code efficiency. Compared with other software tools, the framework provides distinctive features, including a unified framework that integrates both 2D and 3D rendering context, comprehensive support of popular data formats and computing platforms, decent rendition quality via the export of vector graphics and page layout customization, and interactive composition of synoptic charts. Based on the foundation libraries, the software can develop web, desktop and mobile applications for meteorological purposes. These applications support the analysis and visualization of numerous data set types, as well as the interactive content creation. MeteoExplorer is an exploratory project that provides forecasters with new techniques for climate diagnosis and forecasts. [ABSTRACT FROM AUTHOR]

The paper compares the final run of Integrated Multi-Satellite Retrieval of Global Precipitation Mission (IMERG) products with India Meteorological Department (IMD) gridded data over the Indian land mass during the southwest monsoon period (June-September) in the period 2014-2017. Spatiotemporal variations of the IMERG are evaluated with IMD rainfall by employing different statistical techniques, and the capability of the IMERG is examined using the categorical skill metrics. A region in central India is the focus in order to study the southwest monsoon's static and dynamic characteristics, such as rainfall distribution and monsoon activity, using the IMERG in conjunction with IMD rainfall data sets. The integrated condensation rate (ICR) was estimated using the specific humidity profiles, and it correlated with the IMERG and IMD rainfall. The IMERG is found to be a potential source for adequately reflecting the ground gauge-gridded data of categorical rainfall amounts, from very light rain (trace-2.4 mm) to very heavy rain (about 115.6-204.4 mm). However, the IMERG does not reflect satisfactorily the extreme heavy rain events (≥ 204.5 mm·day-1) during the study period. The significant correlation between IMERG/IMD rainfall and the ICR suggests that improved adjustments methods are required for better results when depicting accurate extreme heavy rainfall events by the IMERG. [ABSTRACT FROM AUTHOR]

The southern Indian state of Kerala experienced exceptionally high rainfall during August 2018, which led to devastating floods in many parts of the state. Prediction and early warning of severe weather events in vulnerable areas is crucial for disaster management agencies in order to protect life and property. In recent years, state‐of‐the‐art numerical weather prediction (NWP) models have been used operationally to predict rainfall over different spatial and temporal scales. In the present paper, predictions based on the National Centre for Medium Range Weather Forecasting (NCMRWF) models (NCUM, NCUM‐R and NEPS) are assessed over Kerala to demonstrate the capabilities of high‐resolution models. It is found that the deterministic NWP model (NCUM and NCUM‐R) forecasts are accurate at shorter lead times (up to Day 3) mainly in terms of timing and, to some extent, intensity. At higher lead times (beyond Day 3), the ensemble‐based probabilistic forecasts are useful and actionable. [ABSTRACT FROM AUTHOR]

This paper reports on a waterspout presiding over the Pearl River Estuary near Zhuhai, China. It is observed mainly using the terminal Doppler weather radar in Hong Kong. A slanting radar reflectivity core and airflow circulating around was seen in the vertical cross‐section across the mesoscale/microscale cyclone associated with the waterspout. The vertical wind profile associated with the cyclone was analysed and found to exhibit characteristics similar to those of a tornado reported in the region in a previous study. The primary characteristic of the waterspout was a circulating flow stronger at higher levels (approximately a few kilometres above ground) than at lower levels. In terms of nowcasting, the performance of a convection‐permitting numerical weather prediction model (2 km horizontal resolution) was analysed. It was found to demonstrate reasonable simulation skill for the mesoscale/microscale cyclone, although the slanting feature was not well predicted. The results of this study can serve as a useful reference for similar studies of waterspouts/tornadoes worldwide. [ABSTRACT FROM AUTHOR]

Owing to the lack of lysimetric data in many regions, the standard Penman–Monteith equation‐adopted by Food and Agriculture Organization of the United Nations (FAO56‐PM model) is usually used to calculate the reference evapotranspiration (ETo). However, as this model needs many meteorological parameters that cannot be easily obtained in many regions, other simple models along with the soft computing models are used to obtain ETo values. The paper presents a comprehensive comparison of 12 soft computing models: gene expression programming (GEP), neuro‐fuzzy with grid partitioning (NF‐GP), neuro‐fuzzy with sub‐clustering (NF‐SC), multivariate adaptive regression spline (MARS), boosted regression tree (BT), random forest (RF), model tree (MT), support vector machine (SVM), SVM‐firefly algorithm (SVM‐FA), extreme learning machine (ELM), neural network‐particle swarm optimization (NN‐PSO) and neural network‐differential evolution (NN‐DE), to estimate daily ETo values in humid regions. Therefore, daily meteorological data from two weather stations (during a 12 year period) were used to assess the models. The obtained results revealed that very good efficiency was obtained from all the applied methods. The temperature‐based SVM‐FA (root mean square error (RMSE) = 0.324 mm, Nash–Sutcliffe co‐efficient (NS) = 0.960) and NF‐GP (RMSE = 0.272 mm, NS = 0.974) models generally provided the best accuracy when estimating the ETo of Sari and Bablosar, two humid stations in northern Iran, respectively. The accuracy ranks of the other models (from best to worst) were found to be: NN‐PSO, NF‐SC, ELM, NN‐DE, MARS, GEP, RF, SVM, BT and MT. Among the radiation‐based models, the NF‐GP provided the best accuracy at estimating the ETo of both stations. The other models were ranked as: ELM, SVM‐FA, NN‐DE, NN‐PSO, MARS, RF, BT, NF‐SC, SVM and MT, respectively. [ABSTRACT FROM AUTHOR]

In an average year (1979–2016), the United States experiences nearly 1,100 tornadoes, which cause a total of 68 fatalities. Annual fatality rates have decreased since the peak in the 1920s, but there is a concern that they could start to rise again with increases in vulnerable populations and the impacts of climate change. It is possible to assess the risk of tornado fatalities using the historical record. However, the rarity of tornadoes and the short period of record may not capture the true risk. One way around this problem is to simulate thousands of years' worth of tornadoes to obtain a broader picture of risk. Previous tornado risk models have distributed tornadoes randomly or used climatology to generate realistic tornado patterns on an annual (or longer) time scale. From an operational standpoint, it would be useful to have a model that distributes tornadoes on a daily time step to enable the forecasting of potential tornado impacts on a given day. The present study introduces one such model that distributes tornadoes using information about the favourability of the atmospheric environment for tornado development: The Tornado Daily Impacts Simulator (TorDIS). The paper demonstrates model utility through 1,000 year simulations over several metropolitan areas and with a comparison between modelled and observed impacts for several high‐impact tornado days. Forecasting potential tornado impacts on a daily time step could allow emergency managers to plan ahead for high‐risk days to prioritize their resources and save lives. [ABSTRACT FROM AUTHOR]

Air temperature conditions affect various aspects of human life through diverse pathways, such as health, agriculture, labour productivity, infrastructure and building constructions, economic production, and so on. To address better efforts at resolving this issue, decision‐making tools need to be developed to identify and prioritize areas where appropriate actions are required. The present paper identifies vulnerable areas in Morocco with a focus on three different aspects: high temperatures, low temperatures and daily temperature amplitude. Maps were created using the National Oceanic and Atmospheric Administration (NOAA)‐developed Climate Forecast System Reanalysis (CFSR) 30 year‐long daily maximum and minimum temperature data set. Several indicators as well as vulnerability indexes were defined and calculated. Maps showing the spatial distribution of these indicators were created. The results show that 78% of Morocco is considered to be non‐vulnerable areas to areas with low vulnerability; and 19% is characterized by moderate vulnerability to the effects of temperature. The remaining areas are characterized by high to extreme vulnerability: the most vulnerable areas are located in the provinces of Figuig, Jerada and Boulemane in the eastern part of the country, as well as in the provinces of Khouribga, Fquih Ben Saleh and Settat in the central part. This implies, for example, that national‐level planners in the agriculture and transport/infrastructure sectors in the central part need to take into account temperature vulnerabilities as they develop sector strategies and plans. [ABSTRACT FROM AUTHOR]

In the absence of convection permitting numerical weather prediction (NWP) ensembles, the most recent deterministic NWP precipitation forecast is usually addressed. However, the exact intensity, location and timing of a deterministic precipitation forecast is not always reliable because of the chaotic nature and complexity of precipitation formation mechanisms. This study examines a way to optimize the use of precipitation forecasts for deterministic NWP models. More specifically, it suggests using a spatially smoothed time‐lagged ensemble (TLE) to obtain more reliable precipitation forecasts. A global NWP model (integrated forecast system—IFS) and a regional convection permitting NWP model (COSMO) over the Eastern Mediterranean during the period 2016–2018 were used for the analysis. First, the paper defines light, light–moderate and moderate intensities for 6 hr accumulated precipitation (6hAP) and investigates the corresponding definitions for 1 hr accumulated precipitation (1hAP). Next, fractional skill score (FSS) are used to estimate the optimal spatial smoothing scale of a deterministic precipitation forecast for the three intensity categories for the 6hAP and 1hAP. The FSS is also used to compare COSMO and IFS deterministic precipitation forecasts, and to analyse the skill degradation with the forecast range. It is quantitatively shown that the useful scale of precipitation forecasts is smaller for larger accumulation time intervals. Finally, precipitation forecasts for TLE are formed from successive smoothed deterministic forecasts and compared with the most recent deterministic forecast. It is found that, on average, TLEs have better skills for both 6hAP and 1hAP. The reason for this improvement in skill is illustrated using a case study as an example. [ABSTRACT FROM AUTHOR]

A millimetre‐wave cloud radar has limitations for observing heavy rainfall because the short wavelength leads to strong attenuation from raindrops. However, recent studies have attempted to estimate the rain rate using attenuation, which becomes greater as the rain intensity increases. The rain‐rate‐retrieval algorithm is developed in the present paper using the Ka‐band cloud radar (KaCR) installed at the Boseong Global Standard Observatory (BGSO) in the Republic of Korea. First, rain profiles were identified using the threshold of reflectivity (Z) and Doppler velocity (DV) averaged for the analysis of layer that minimized the effects of the receiver saturation and classified as low or high rain‐rate cases using the averaged DV. The reflectivity and rain‐rate (Z‐R) relationship was then derived for low rain‐rate cases that showed insignificant effects for signal attenuation. On the other hand, the attenuation and rain‐rate (A‐R) relationship was derived using high rain‐rate cases that showed the dominant effects of attenuation. The attenuation was calculated using a reflectivity gradient between the upper and lower boundaries of the attenuated layer. Finally, the rain‐rate‐retrieval algorithm was designed using the derived Z‐R and A‐R relationship and then applied back to the KaCR. The estimated rain rate in the KaCR was a similar trend to the observed rain rate in the optical rain gauge (ORG), but slightly underestimated. [ABSTRACT FROM AUTHOR]

The application of a novel method of adaptive neuro‐fuzzy inference system (ANFIS) for the prediction of air temperature is investigated. The paper discusses the improvement of the ANFIS when used with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR) and differential evolution (DE). For this purpose, three input of multiple variables are selected in order to predict monthly minimum, average and maximum air temperatures for 34 meteorological stations in Iran. The co‐efficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) are used as evaluation criteria. A comparison of suggested fuzzy models indicates that the ANFIS with the GA has the best performance in the prediction of maximum temperatures. It decreases the RMSE of the classic ANFIS model in the validation stage from 1.22 to 1.12°C for Mashhad, from 1.26 to 1.01°C for Zahedan, from 1.20 to 0.98°C for Ahvaz, from 1.76 to 1.24°C for Rasht and from 1.21 to 0.95°C for Tabriz. [ABSTRACT FROM AUTHOR]