Hasil untuk "Meteorology. Climatology"

A. Wood, E. Maurer, Arun Kumar et al.

Iain Stewart

F. Baret, O. Hagolle, B. Geiger et al.

This article describes the algorithmic principles used to generate LAI, fAPAR and fCover estimates from VEGETATION observations. These biophysical variables are produced globally at 10 days temporal sampling interval under lat–lon projection at 1/112° spatial resolution. After a brief description of the VEGETATION sensors, radiometric calibration process, based on vicarious desertic targets is first presented. The cloud screening algorithm was then fine tuned using a global network of cloudiness observations. Atmospheric correction is then achieved using the SMAC code with inputs coming from meteorological values of pressure, ozone and water vapour. Aerosol optical thickness is derived from MODIS climatology assuming continental aerosol type. The Roujean BRDF model is then adjusted for red, near infrared and short wave infrared bands used to the remaining cloud free observations collected over a time window of ±15 days. Outliers due to possible cloud contamination or residual atmospheric correction are iteratively eliminated and prior information is used to get more robust estimates of the three BRDF kernel coefficients. Nadir viewing top of canopy reflectance in the three bands is input to the biophysical algorithm to compute the products at 10 days sampling interval. This algorithm is based on training neural networks over SAIL+PROPSPECT radiative transfer model simulations for each biophysical variable. Details on the way the training data base was generated and the neural network designed and calibrated are presented. Finally, theoretical performances are discussed. Validation over ground measurement data sets and inter-comparison with other similar biophysical products are presented and discussed in a companion paper. The CYCLOPES products and associated detailed documentation are available at http://postel.mediasfrance.org.

H. Cleugh, R. Leuning, Q. Mu et al.

Z. Şen

Alys C. Thomas, J. Reager, J. Famiglietti et al.

C. Toté, Domingos Patricio, H. Boogaard et al.

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

Hang Deng, Dong Li, Shanshan Cai et al.

Abstract Under the influence of factors such as climate and land use changes, it is highly useful to investigate the spatio-temporal occurrence characteristics of forest fires using remote sensing data. This study utilized long-term remote sensing data on Active Fire Spots (AFSs), Burned Areas (BA), and Land Cover Types (LCT) in Yunnan Province. Through pixelization of AFSs, spatial extraction, and spatio-temporal clustering, 39,101 Forest Fire Events (FFEs) were identified. The results indicate that FFEs in Yunnan Province exhibit spatio-temporal clustering, with an overall annual fluctuating decline trend. The clustering is more pronounced in spring and winter, with a delayed temporal span. Over 88% of FFEs are concentrated in southern Yunnan, and the frequent occurrence areas have shifted eastward in recent years. This study deepens the understanding of the spatio-temporal dynamics of forest fires and provides a basis for regional forest fire management to promote sustainable development in related fields.

Yu Han, Yi Liu, Yaping Zhang et al.

By using the hourly visibility, temperature, pressure, humidity, wind, and atmospheric particulate concentration data in Chongqing from 2015 to 2023, the characteristics of low visibility (visibility <1000 m) in Chongqing and the influence of various factors on low visibility in Chongqing were analyzed. The visibility prediction model was established by using the neural network method, and the effect of introducing the PM2.5 concentration factor on low visibility prediction was analyzed and compared. Findings: Low visibility in Chongqing is dominated by precipitation low visibility (PLV), followed by fog low visibility (FLV), with the least proportion of fog-haze mixed low visibility (FHLV). However, as visibility decreases further, the proportion of fog with low visibility increases significantly. The average visibility when fog occurs is lower than that when precipitation occurs and also much lower than that of fog-haze mixed, indicating that low visibility is more affected by atmospheric water vapor. Over the past decade, as air pollutants have decreased each year, the proportion of fog and FHLV has also trended downward. The proportion of fog increases significantly in winter, and the low visibility below 200 m is mainly caused by fog in winter, while the increase of precipitation in June is the main cause of low visibility in this month. The diurnal variation of mean visibility under precipitation is relatively small. In contrast, the mean visibility during fog and fog-haze mixed conditions is lower at night than during the day. The higher occurrence rate of these two types of low visibility conditions at night is a significant factor contributing to reduced visibility during nighttime. Atmospheric humidity, temperature, and particulate matter concentration are important factors affecting visibility, and visibility decreases significantly with the increase of PM2.5 when relative humidity (RH) is less than 70%, and PM2.5 has a lower effect on visibility when RH is greater than 70%. The forecast effect of introducing the PM2.5 concentration factor into the objective forecast model of visibility is better than that of not introducing the factor. The effect of introducing this factor is better than that of not introducing it, especially in the fall.

Lauren A. James, Helen F. Dacre, Natalie J. Harvey

Abstract Producing quantitative volcanic ash forecasts is challenging due to multiple sources of uncertainty. Careful consideration of this uncertainty is required to produce timely and robust hazard warnings. Structural uncertainty occurs when a model fails to produce accurate forecasts, despite good knowledge of the eruption source parameters, meteorological conditions and suitable parameterizations of transport and deposition processes. This uncertainty is frequently overlooked in forecasting practices. Using a Lagrangian particle dispersion model, simulations with varied output spatial resolution, temporal averaging period and particle release rate are performed to quantify the impact of these structural choices. This experiment reveals that, for the 2019 Raikoke eruption, structural choices give measurements of peak ash concentration spanning an order of magnitude, significantly impacting decision‐relevant thresholds used in aviation flight planning. Conversely, along‐flight dosage estimates exhibit less sensitivity to structural choices, suggesting it is a more robust metric to use in flight planning. Uncertainty can be reduced by eliminating structural choices that do not result in a favourable level of agreement with a high‐resolution reference simulation. Reliable forecasts require output spatial resolution ≤ 80 km, temporal averaging periods ≤ 3 h and particle release rates ≥ 5000 particles/h. This suggests that simulations with relatively small numbers of particles could be used to produce a large ensemble of simulations without significant loss of accuracy. Comparison with previous Raikoke simulations indicates that the uncertainty associated with these constrained structural choices is smaller than those associated with satellite constrained eruption source parameter and internal model parameter uncertainties. Thus, given suitable structural choices, other epistemic sources of uncertainty are likely to dominate. This insight is useful for the design of ensemble methodologies which are required to enable a shift from deterministic to probabilistic forecasting. The results are applicable to other long‐range dispersion problems and to Eulerian dispersion models.

Zhe Feng, L. Leung, S. Hagos et al.

The changes in extreme rainfall associated with a warming climate have drawn significant attention in recent years. Mounting evidence shows that sub-daily convective rainfall extremes are increasing faster than the rate of change in the atmospheric precipitable water capacity with a warming climate. However, the response of extreme precipitation depends on the type of storm supported by the meteorological environment. Here using long-term satellite, surface radar and rain-gauge network data and atmospheric reanalyses, we show that the observed increases in springtime total and extreme rainfall in the central United States are dominated by mesoscale convective systems (MCSs), the largest type of convective storm, with increased frequency and intensity of long-lasting MCSs. A strengthening of the southerly low-level jet and its associated moisture transport in the Central/Northern Great Plains, in the overall climatology and particularly on days with long-lasting MCSs, accounts for the changes in the precipitation produced by these storms. The central United States has exhibited increased extreme precipitation. Here, using satellite, radar, and rain-gauge data, Feng et al. show that springtime total and extreme rainfall trends are linked to increased intensity and frequency of long-lived Mesoscale Convective Systems.

Ishanch Qutbudin, M. Shiru, A. Sharafati et al.

We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5° resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 °C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming.

L. Busschaert, Shannon de Roos, W. Thiery et al.

Abstract. Global soil water availability is challenged by the effects of climate change and a growing population. On average 70 % of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the FAO crop growth model AquaCrop version 6.1. The model is run at 0.5° lat × 0.5° lon resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the performance of AquaCrop surface soil moisture (SSM) simulations using historical meteorological input from two ISIMIP3 forcing datasets is evaluated with satellite-based SSM estimates. When driven by ISIMIP3a reanalysis meteorology for the years 2011–2016, daily simulated SSM values have an unbiased root-mean-square difference of 0.08 and 0.06 m3m−3 with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively. When forced with ISIMIP3b meteorology from five Global Climate Models (GCM) for the years 2011–2020, the historical simulated SSM climatology closely agrees with the climatology of the reanalysis-driven AquaCrop SSM climatology as well as the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031–2060 and 2071–2100) is compared to the baseline period of 1985–2014, to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 67 mm year–1 (+30 %) under a high emission scenario Shared Socioeconomic Pathway (SSP) 3-7.0. Central and southern Europe are the most impacted with larger Inet increases. The interannual variability of Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1-2.6), the increase in Inet will stabilize around 40 mm year–1 towards the end of the century and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.

Liang Zhang, Qiang Zhang, Hongli Zhang et al.

Abstract The surface evapotranspiration (ET) process is the key link in the interaction between land and atmosphere. However, the influence of different environmental factors on ET over transitional climate zones and the physical pattern of the interaction between multiple factors remain unclear. Therefore, based on the continuous observation data during the vegetation growing season of a typical grassland in the Semi‐Arid Climate and Environment Observation of Lanzhou University (SACOL) station from 2007 to 2012, the influence pattern of multiple environmental factors on ET over China's transitional climate zone was analysed. Each environmental factor exhibited significant seasonal and interannual variations. The mean value of ET was 1.67 mm day−1. Although the maximum values of sensible heat flux and vapour pressure deficit occurred in April and June, respectively, the maximum values of other environmental factors appeared from July to August. Net radiation, soil moisture, and normalized difference vegetation index (NDVI) were the main controlling factors of ET over the grassland, with correlation coefficients of 0.54, 0.52, and 0.46, respectively. The analysis of multiple environmental factors showed that when soil moisture, wind speed, net radiation, and NDVI reached 0.2 m3 m−3, 2 m s−1, 100 W m−2, and 0.2, respectively, ET varied in contrast with vapour pressure, vapour pressure deficit, and air temperature under the influences of weather processes, land–atmosphere coupling, and drought stress. These findings deepen our understanding of the role of ET in the land–atmosphere coupling process over the transitional climate zone in China.

Zitong Li, Weihang Liu, Tao Ye et al.

Understanding the changes in the frequency and intensity of compound agroclimatic extremes is important for studying the resilience of the food system under anthropogenic warming. However, the spatiotemporal variation of compound agroclimatic extremes for specific crops, and the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations in reproducing them, have largely been under-addressed. Herein, we have investigated the spatiotemporal variation of the occurrences and intensities of four compound agroclimatic extremes (hot-wet, hot-dry, cold-wet, cold-dry) for maize harvested areas in China during 1990–2014 and examined the capability of the CMIP6 to capture these variations. The results did not reveal any significant trends but there was a pronounced interannual volatility in both the occurrence and intensity of compound agroclimatic extremes. Seventy one percent of the maize harvested areas in China experienced at least one compound extreme event during the study period. Hotspots for high occurrence and high intensity included the major plains in the Huang-Huai-Hai and southern China maize zones. In general, five general circulation models (GCMs) from the CMIP6 relatively poorly captured the variation of the occurrence and intensity, and reported lower interannual volatility and less regional disparity compared to the observations, and showed weakness in capturing time-specific events. At the national scale, the five GCM ensemble mean outperformed any single model, and no single GCM outperformed any other individual GCMs. The best model differed according to the specific region of interest and the type of event. Our results highlight the need to improve field management decisions and adopt adaptation strategies in accord with local conditions to reduce the potential impacts on food systems.

Xuecheng Fu, Lei Yao, Shuo Sun

The urbanization process leads to the enhancement of the urban heat island (UHI) effect, and the high temperature brought by it exacerbates the risk of heat exposure and seriously endangers human health. Analyzing the spatiotemporal characteristics and levels of heat exposure risk is important for formulating heat risk prevention and control measures. Therefore, this study analyzes the spatiotemporal characteristics of heat exposure risk based on the UHI footprint (FP) and explores the relationship between it and urbanization factors in the Beijing–Tianjin–Hebei (BTH) region from 2000 to 2020, and obtains the following conclusions: (1) The BTH region suffers from severe UHI problems, with FP ranging from 6.05 km (Chengde) to 32.51 km (Beijing), and the majority of cities show significant trends of FP increase. (2) With the increase in FP, massive populations are exposed within the heat risk areas, with the average annual population at risk across cities ranging from 269,826 (Chengde) to 166,020,390 (Beijing), with a predominance of people exposed to high risk (more than 65% of the total) and generally showing increasing trends. (3) The population at risk of heat exposure is significantly correlated with urbanization factors, indicating that urbanization is an important reason for the increase in the risk population and the enhancement of the risk level. These results suggest that with the continuous urbanization process, the heat exposure risk problem faced by cities in the BTH region will persist and gradually worsen, which must be paid attention to and effective mitigation measures must be taken.

Roxana ROȘCĂNEANU, Robert STRECHE, Filip OSIAC et al.

In recent years, the Internet of Things concept has rapidly spread in most fields because of the benefits it offers, motivating viticulturists to implement new technologies that increase crop production and quality, as well as streamline production costs. The study’s purpose is to monitor, using Internet of Things technology, two methods of identifying vine-specific diseases, which can be determined by environmental conditions (temperature, humidity, rainfall) or by analyzing diseased leaves from the vine. The first method is associated with a field study that involves placing Internet of Things sensors inside crops to measure environmental and plant parameters, which are then sent and stored in the Cloud. Based on these parameters, a correlation is made with the values that determine the occurrence of a specific vine disease (powdery mildew, downy mildew, and grey rot). The second method involves the use of Unmanned Aerial Vehicle imaging to take images containing healthy and diseased leaves from different parts of the vine. To analyze these images, a web page has been developed integrating a machine learning algorithm that detects the leaf state from the drone image footage. After the analysis all the values are stored in a database and the results are displayed as graphs and charts that are visualized by the viticulturist so that he can take the necessary actions. This study is an important step in the implementation of Internet of Things technology in viticulture, helping to monitor the main environmental and plant parameters, as well as detecting the occurrence of diseases among the vine cultures.

Zieger Bertalan, Mursula Kalevi

The large-scale photospheric magnetic field is commonly thought to be mainly dipolar during sunspot minima, when magnetic fields of opposite polarity cover the solar poles. However, recent studies show that the octupole harmonics contribute comparably to the spatial power of the photospheric field at these times. Also, the even harmonics are non-zero, indicating that the Sun is hemispherically asymmetric with systematically stronger fields in the south during solar minima. We present here an analytical model of two eccentric axial dipoles of different strength, which is physically motivated by the dipole moments produced by decaying active regions. With only four parameters, this model closely reproduces the observed large-scale photospheric field and all significant coefficients of its spherical harmonics expansion, including the even harmonics responsible for the solar hemispheric asymmetry. This two-dipole model of the photospheric magnetic field also explains the southward shift of the heliospheric current sheet observed during recent solar minima.

Aitor Atencia, Yong Wang, Alexander Kann et al.

Lagrangian extrapolation nowcasting is still the most accurate technique for predicting precipitation for the first hours despite the improvements achieved by data assimilation in high-resolution numerical weather prediction models. Yet, the combination of both sources by blending techniques is necessary for creating a seamless forecast for the nowcasting range. The blending is usually carried by adding one forecast to the other one multiplied previously by a weight. This weight can be computed climatologically or taking into account parameters such as the synoptic situation or the intensity of precipitation from the different sources of forecast. In this paper, a general formulation for computing the blending weight is introduced. This new formulation allows not only to introduce new factors such as the localization (pixel scale) but also to isolate the effect of some parameters such as the flow-dependency (synoptic situation). A sensitivity study of these parameters, separated and combined, is carried out for a whole month. The results show the relative importance separately of each effect (localization and flow-dependency) but, most importantly, the improvements in terms of combining the two sources of information when these two factors are used together in the computation of the blending weights.