Meteorological Applications最新文献

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.

The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policymakers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past 40 years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydropower, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long term. This article introduces this service and the resulting dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO-E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been preferred upon models' accuracy. However, the comparison with ENTSO-E data shows that the models provide plausible energy indicators and, in particular, allow comparing climate variability effects on power demand and generation in a harmonized manner all over Europe.

The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object-based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high-impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one-way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object-based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall.

Nowadays, several global ensembles (GEs) which consist of several tens of members are being run operationally. In order to locally improve the probabilistic forecasts, various forecasting centers and research institutes utilize the GEs as initial and boundary conditions to drive regional convection permitting ensembles (RCPEs). RCPEs demand significant computer resources and often a limited number of ensemble members is affordable, which is smaller than the size of the driving GE. Since each RCPE member obtains the initial and boundary conditions from a specific GE member, there are many options to select the GE members. The study uses the European Centre for Medium-Range Weather Forecasts (ECMWF) GE consisting of 50 members, to drive 20 members of COSMO model RCPE over the Eastern Mediterranean. We compare various approaches for automatic selection of the GE members and propose several optimal methods, including a random selection, which consistently lead to a better performance of the driven RCPE. The comparison includes verification of near surface variables and precipitation using various verification metrics. The results are validated using several methods of model physics perturbation. Besides the selection of the optimal ensemble configurations, we show that at high precipitation intensities spatial up-scaling is recommended in order to obtain useful probabilistic forecasts.

The need for representative and accurate climate data such as precipitation useful for drought monitoring has been rapidly increasing among policymakers and practitioners to tackle climate-change-induced drought events. Hence, the objective of this article is to evaluate the drought monitoring performance of global precipitation products for the three wet seasons and rainfall regions in Ethiopia. Drought indices were calculated using the Standardized Precipitation Index (SPI) at 3-month timescale for Belg (March–May) and Autumn (September–November) seasons and at 4-month timescale for the Kiremt (June–September) seasons. Data products were evaluated for their accuracy in representing drought magnitude, geographical coverage and frequency using quasi-objective (visual inspection), and frequency and correlation analysis methods. The performance of gridded precipitation products was compared against the SPI value computed for 126 reference stations and the Ethiopian satellite-gauge merged precipitation data. Precipitation products showed different levels of performance in representing the magnitude, frequency and geographical coverage of drought events for the three wet seasons and rainfall regions. None of the data products outperformed in representing the occurrence of drought for all three wet seasons and the corresponding three rainfall regions. However, the Ethiopian merged precipitation, Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) precipitation products are relatively better than others. The study results generally indicate that no single data outperform the other precipitation products in representing the complex spatiotemporal characteristics of drought events in a mountainous region like Ethiopia.

Creating a forecast that is seamless across time yet is optimal at each forecast validity time is often achieved by blending forecasts from multiple Numerical Weather Prediction models (or using other forecast sources, such as an extrapolation nowcast). With the increasing usage of convection-permitting ensemble models at shorter lead times, the blending of these forecasts with longer-range ensemble models with parameterized convection can lead to a clear transition from one forecast source to another. This is particularly noticeable when visualizing the evolution of the gridded forecast. Calibrating the forecast sources with a common truth prior to blending provides a method of improving forecast skill whilst also unifying the characteristics of the forecasts to create a smoother blend throughout the evolution of the forecast. In this work, a non-parametric method for calibrating the reliability of the forecast without degrading the forecast resolution is assessed for its usability for gridded precipitation rate and total cloud amount forecasts. Reliability is markedly improved resulting in a similar skill between forecast sources during the blending period. Further refinements to the technique removed artefacts in the gridded forecasts. Caveats, including a reduction in sharpness following calibration, are also presented.

It is important for territorial spatial planning to master meteorological disaster risk under the conditions of climate change and then carry out risk adaptation planning according to local conditions. Taking Beijing, a large city in China, as an example, a meteorological disaster risk assessment model was established based on the framework of hazard factors, disaster-bearing body exposure, and vulnerability of underlying surface. Combined with 11 years of observation data from 293 high-density weather stations, the rainstorm and high-temperature risks of urban, agriculture, and ecological spaces were studied. The results show that (1) rainstorms and high-temperature are mainly distributed in the built-up areas of plain towns, which are the climate risk factors that need to be considered. (2) The central city of Beijing is at a high risk of rainstorms and high temperature, indicating that the underlying surface and disaster-bearing body are highly vulnerable to meteorological disasters. (3) In suburbs with agricultural land, there is a certain rainstorm risk in Fangshan and Daxing districts, and a risk of high temperatures in the southern part of Tongzhou and Daxing. (4) The risk of high temperatures in the ecological space (eco-zone) is relatively low, but the rainstorm risk is relatively high in Pinggu and Miyun. (5) The strategies of coping with rainstorm and high-temperature disaster risk in Beijing's territorial space planning were discussed.

Renewable power generation from wind and solar energy is strongly dependent on the weather. To plan future sustainable energy systems that are robust to weather variability, a better understanding of why and when periods of low wind and solar power output occur is valuable. We call such periods of low wind speed and insolation “Dunkelflauten”, the German word for “dark wind lulls”. In this article, we analyse the meteorological conditions during Dunkelflauten in Germany by applying the concept of weather regimes. Weather regimes are quasi-stationary, recurrent and persistent large-scale circulation patterns that explain multi-day atmospheric variability (5–15 days). We use a regime definition that allows us to distinguish four different types of blocked regimes, characterized by high-pressure situations in the North Atlantic-European region. We find that Dunkelflauten in Germany occur mainly in winter when the solar power output is low due to the seasonal cycle of solar irradiance and wind power output drops for several consecutive days. A high-pressure system over Germany, associated with the European Blocking regime, is responsible for most of the Dunkelflauten. Dunkelflauten during the Greenland Blocking regime are associated with colder temperatures than usual, causing higher electricity demand, and would present a particular challenge as space heating becomes electrified in the future. Furthermore, we show that Dunkelflauten occur predominantly when a weather regime is well established and persists longer than usual. Our study provides novel insight into the occurrence and meteorological characteristics of Dunkelflauten, which is essential for planning resilient energy systems and supporting grid operators to prepare for potential shortages in supply.

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.

We assess the skill of the fully coupled lagged ensemble forecasts from GloSea5-GC2, for the sub-seasonal to seasonal (S2S) timescale up to 4 weeks, with the aim of understanding how these forecasts might be used in a Ready-Set-Go style decision-making framework. Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) are used to seamlessly verify these ensemble forecasts up to monthly timescales whereby forecast and observed precipitation fields are summed over a sequence of increasing lead time accumulation windows (LTAWs), from 1d1d up to 2w2w. Results show that model biases grow with increasing LTAW and with ensemble member age. The S2S model exhibits both wet and dry biases across different parts of the Indian domain. The S2S model error grows from around 10 mm for a 24-h accumulation to 50 mm for the 2-week LTAWs. The actual skill and potential skill of the ensemble forecasts reveal that the potential skill is not always greater than actual skill everywhere. The sensitivity to the number and age of ensemble members was tested, with potential skill showing more impact from the exclusion of older members at all LTAWs. We conclude that the older lagged members do not necessarily add value by being included in the short to medium range or even for the extended range forecasts. GloSea5-GC2 shows some skill in detecting large accumulations, which are not always tied to locations where they are climatologically frequent.