Meteorological Reanalysis Data Research Articles

Global variation in ecoregion flammability thresholds

Anthropogenic climate change is altering the state of worldwide fire regimes, including by increasing the number of days per year when vegetation is dry enough to burn. Indices representing the percent moisture content of dead fine fuels as derived from meteorological data have been used to assess geographic patterns and temporal trends in vegetation flammability. To date, this approach has assumed a single flammability threshold, typically between 8 and 12%, controlling fire potential regardless of the vegetation type or climate domain. Here we use remotely sensed burnt area products and a common fire weather index calculated from global meteorological reanalysis data to identify and describe geographic variation in fuel moisture as a flammability threshold. This geospatial analysis identified a wide range of flammability thresholds associated with fire activity across 772 ecoregions, often well above or below the commonly used range of values. Many boreal and temperate forests, for example, can ignite and sustain wildfires with higher estimated fuel moisture than previously identified; Mediterranean forests, in contrast, tend to sustain fires with consistently low estimated fuel moisture. Statistical modelling showed that flammability thresholds derived from burnt area are primarily driven by climatological variables, particularly precipitation and temperature. Our analysis also identified complex associations between vegetation structure, fuel types, and climatic conditions highlighting the complexity in vegetation–climate–fire relationships globally. Our study provides a critical, necessary step in understanding and describing global pyrogeography and tracking changes in spatial and temporal fire activity.

MALTA: A Zonally Averaged Global Atmospheric Transport Model for Long‐Lived Trace Gases

AbstractWe present a two‐dimensional, zonally averaged global model of atmospheric transport named MALTA: Model of Averaged in Longitude Transport in the Atmosphere. It aims to be accessible to a broad community of users, with the primary function of quantifying emissions of greenhouse gases and ozone depleting substances. The model transport is derived from meteorological reanalysis data and flux‐gradient experiments using a three‐dimensional transport model. Atmospheric sinks are prescribed loss frequency fields. The zonally averaged model simulates important large‐scale transport features such as the influence on trace gas concentrations of the quasi‐biennial oscillation and variations in inter‐hemispheric transport rates. Stratosphere‐troposphere exchange is comparable to a three‐dimensional model and inter‐hemispheric transport is faster by up to 0.3 years than typical transport times of three‐dimensional models, depending on the metric used. Validation of the model shows that it can estimate emissions of CFC‐11 from an incorrect a priori emissions field well using three‐dimensional (3D) mole fraction fields generated using a different 3D model than which the flux gradient relationships were derived. The model is open source and is expected to be applicable to a wide range of studies requiring a fast, simple model of atmospheric transport and chemical processes for estimating associated emissions or mole fractions.

A Novel Double Machine Learning Strategy for Producing High‐Precision Multi‐Source Merging Precipitation Estimates Over the Tibetan Plateau

AbstractPrecipitation estimation over the Tibetan Plateau is a critical but challenging task due to sparse gauges and high altitudes. Traditional statistic methods are often insufficient to characterize the nonlinear relationship between different precipitation information, while machine learning techniques, particularly deep learning algorithms, offer a novel and powerful approach to improve the merging accuracy of multi‐source precipitation data by efficiently capturing their spatiotemporal dynamics features. This study introduced a novel strategy called Double Machine Learning (DML), which integrates meteorological information, satellite retrievals, and reanalysis data to produce a high‐precision multi‐source merging precipitation product at 0.1° × 0.1°, daily resolution for the Tibetan Plateau. The quantitative evaluation of DML was accomplished using both auto‐meteorological gauges and independent observations. Statistical scores indicate that the new DML‐based merging product apparently outperforms three widely‐used precipitation datasets (IMERG‐Final, GSMaP‐Gauge and ERA5) over the Tibetan Plateau. The proposed DML strategy effectively integrates the advantages of traditional machine learning and deep learning, significantly enhancing the algorithmic robustness and merging accuracy, particularly at medium‐high rain rates in summer. Furthermore, the contributions of multi‐source inputs to the final merging effect was systematically analyzed. It is found that meteorological information, as an auxiliary variable in DML, plays a crucial role in identifying rainy events and adjusting the bias of precipitation estimates, especially over those ungauged regions. This study affirms the call for improving the multi‐source precipitation estimates by combining different machine learning approaches. The new merging precipitation product reported here is recommended for hydrometeorological users of the Tibetan Plateau science community.

Measurement report: Nocturnal subsidence behind the cold front enhances surface particulate matter in plains regions: observations from the mobile multi-lidar system

Abstract. A multi-lidar system, mounted in a vehicle to monitor the profiles of temperature, wind, and particle optical properties, was utilized to investigate the winter fine particulate matter (PM2.5) pollution from a vertical perspective in four cities in China in winter 2018. We observed the enhancement of surface nocturnal PM2.5 in two typical plains cities (Changzhou and Wangdu), which was attributed to the subsidence of PM2.5 transported from upstream polluted areas, with the wind turning north and downdrafts dominating. Combining the observed surface PM2.5, the reanalysis meteorological data, and the GEOS-Chem model simulation, we revealed the transport nocturnal PM2.5 enhancement by subsidence (T-NPES) events that occurred frequently in the two cities, with percentages of 12.2 % and 18.0 %, respectively, during December 2018–February 2019. Furthermore, the GEOS-Chem model simulation further confirmed the ubiquity of winter T-NPES events on a large scale, including North China Plain and the Yangtze River Delta. Process analysis revealed that the subsidence was closely correlated with the southeasterly movement of the high-pressure system and the passage of the cold front, resulting in the increase of temperature aloft, a stronger inversion layer, and further PM2.5 accumulation in the atmospheric boundary layer. Thus, a conceptual model of the T-NPES events was proposed to highlight this surface PM2.5 enhancement mechanism in these plains regions. However, it did not apply to the two cities in the basin region (Xi'an and Chengdu) due to the obstruction of the weather system movement by the mountains surrounding the basin.

Refined troposphere delay models by NWM ray-tracing for pseudolite positioning system and their performance assessment

Pseudolite positioning system can enhance GNSS (Global Navigation Satellite System) by improving satellite geometry and providing services independently in the GNSS unavailable environment. Many error items of pseudolite positioning system, e.g., tropospheric delay error, need to be corrected by an accurate model. While several tropospheric delay models suitable for pseudolite positioning systems have been proposed, the key parameters indicating the upper boundaries for tropospheric refraction are fixed to an experimental value in these models. In this paper, we studied the spatiotemporal characteristics of the upper boundary heights for hydrostatic and wet tropospheric delay based on the ERA5 global meteorological reanalysis data. With the refined height boundary term, we refine four existing tropospheric delay models for pseudolite positioning systems, including RTCA, MRTCA, Bouska and Hopfield models. To evaluate the performance of the refined models, we selected 12 airports distributed globally as experimental areas and analyzed the error characteristics of the four refined tropospheric models and the LTC model (independent of height). The experimental results show that when the slant distance between the pseudolite base station and the receiver is constant at 5 km, the MRTCA model has the best performance, with an RMSE (root mean square error) of about 0.15 m. When the elevation angle between the pseudolite base station and the receiver is constant, the RMSE of the LTC model is the smallest at the low elevation angle, and the RMSE of the MRTCA model is the smallest at the high elevation. The RMSE of pseudolite tropospheric delay models show seasonal variation.

The impact and mechanism analysis of preceding sea surface temperature anomalies on summer runoff in the Yangtze River basin and its southern region

AbstractThe Yangtze River basin (YRB) and its southern region in China (20°–34°N, 104°–123°E, YRBSC) are highly susceptible to climate change and experience extreme hydrological events. To understand the spatial and temporal distribution of summer runoff in these regions, a statistical diagnosis method was applied using monthly mean runoff grid data, global Sea Surface Temperature (SST) data and meteorological reanalysis data from 1980 to 2022. The analysis revealed that variations in the isotropic phase within the YRBSC and the north–south inverse phase with the Yangtze River as the boundary are the main modes of summer runoff. Furthermore, a strong correlation was observed between winter SST anomalies (SSTAs) and late summer runoff in the YRBSC, as determined through singular value decomposition (SVD). In the first type of positive SSTA years, the eastward advance of the South Asian high pressure (SAH) and westward shift of the subtropical high pressure (SH) result in sufficient water vapour, strong upward movement and increased summer runoff. The second type of positive SSTA years exhibits a westward retreat of the SAH, upward movement north of 28°N, and downward movement between 20°N and 28°N. These conditions, combined with water vapour intermixing and dispersion, lead to a northward increase and southward decrease of summer runoff in the YRBSC, with the boundary at 28°N. Additionally, the study analysed the extreme drought situation observed in the YRB during the summer of 2022. The findings of this research provide valuable insights for ecological environmental protection, water resource planning and management in the region.

Assessing the hydroclimate changes in Western Himalayas during the Little Ice Age

The Little Ice Age (LIA) was a period of most recent glacial advancement and had pronounced cooling effect in the North Atlantic region. Synchronous hydroclimate changes are also reported from the Himalayas, however owing to the heterogeneity within the proxy reconstructions, their relationship with LIA cooling is unclear. Varied topography, huge glacial mass, and multiple moisture sources (both from the Indian Summer Monsoon (ISM) and the Westerlies) makes understanding of the impact of LIA cooling on this region ambiguous. In this study, we review and assess the existing paleoclimatic proxy records for a comprehensive analysis of the regional response of the Western Himalayas to LIA cooling. Using the existing meteorological reanalysis data for back trajectory analysis for the last 20 years, the Western Himalayan region was classified into three different zones based on the relative percentage of moisture-bearing-wind-source contribution. The upper Western Himalayas receive most of the moisture from the Westerlies, both Middle and Lower Western Himalayas receive majority of rainfall from the ISM, with a relatively higher contribution of Westerlies in the Lower Western Himalayas. Comparison of reconstructions using Principal Component Analysis reveal consistent high moisture conditions during the LIA, with increased winter precipitation and decreased summer precipitation coherently in all the records. Spectral analysis of the available proxy records and various climate forcing for LIA cooling show similar dominant frequency, attesting that the LIA cooling drove the hydroclimate changes in the Western Himalayan region. External forcings such as decreased solar activity and increased volcanic activity caused cooling, influenced the Inter Tropical Convergence Zone, and resulted in weaker summer rainfall during the LIA. Synchronous changes in the North Atlantic Oscillations and El Niño–Southern Oscillation records with precipitation records suggest a link between “monsoon breaks” and enhanced Westerly intensity and an intensified winter precipitation in this region.

Prediction of Autumn Ozone Concentration in the Pearl River Delta Based on Machine Learning

Based on the observation data of the daily maximum 8-hour ozone (O3) average concentration[MDA8-O3, ρ(O3-8h)] and meteorological reanalysis data in the Pearl River Delta Region from 2015 to 2022, four machine learning methods, i.e., support vector machine regression (SVR), random forest (RF), multi-layer perceptron (MLP), and lightweight gradient boosting machine (LG) were used to establish MDA8-O3 prediction models. The results showed that the SVR model had the best prediction performance on MDA8-O3 during the whole year, the coefficient of determination (R2) reached 0.86, and the root mean square error (RMSE) and mean absolute error (MAE) were 16.3 μg·m-3 and 12.3 μg·m-3, respectively. The prediction performance of the SVR model in autumn was still slightly better than that of LG and MLP, with R2,RMSE,and MAE values of 0.88, 19.8 μg·m-3,and 16.1 μg·m-3, respectively. The RF model performed the worst in the autumn prediction. In addition, the models trained by data from the whole year had better prediction ability on autumn MDA8-O3 than that of those only trained by autumn data, and the R2 differed 0.08-0.14.

Creep features and mechanism of active-layer detachment slide on the Qinghai-Tibet Plateau by InSAR

An active-layer detachment slide (ALDS) occurred on September 21, 2018, in the Fenghuoshan mountains of the Qinghai-Tibet Plateau (QTP) (34°39.1′N, 92°53.5′E). With the Sentinel-1A image from Copernicus Open Access Hub, we use small baseline subset to achieve the time series deformation map to analyze the thermo-spatial creep feature, motion pattern, trigger mechanism, and correlation of environmental changes in the ALDS. The SBAS (the Small Baselines Subset) results show that the trailing part of ALDS has the largest downward deformation rate; however, the leading area was small, and the creep feature shows a clear seasonal change corresponding to the freeze-thaw cycle. We also divide the motion pattern into three stages: moderate creep, steady creep, and rapid collapse, based on the deformation rate. Meteorological observation and reanalysis data, as well as borehole data, show that heavy precipitation in the summer of 2017 and 2018 promote the formation of underground ice, while high air temperatures allow the thaw plane to reach the ice-rich zone, and confined water generated by the two-way freezing process result in ALDS. Moreover, there exists a temporal delay of approximately one month in the association between deformation rate and both precipitation and temperature. Furthermore, there is a clear correlation between variations in thawing depth and deformation, which serves as the primary catalyst for ALDS in permafrost regions. Finally, we also identify that ALDS is a mixed-type landslide and that cumulative deformation and creep damage play the main roles in triggering ALDS.

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems.

Climate warming is expected to increase global methane (CH4 ) emissions from wetland ecosystems. Although insitu eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4 ) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4 , with increases in FCH4 of ca. 10% or higher found in the fall from 1981-1989 to 2010-2018. The annual trends were driven by increases during summer and fall, particularly at high-CH4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10-40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.

National ground-level NO2 predictions via satellite imagery driven convolutional neural networks

Outdoor air pollution, specifically nitrogen dioxide (NO2), poses a global health risk. Land use regression (LUR) models are widely used to estimate ground-level NO2 concentrations by describing the satellite land use characteristics of a given location using buffer distance averages of variables. However, information may be leaked in this approach as averages ignore the variances within the averaged region. Therefore, in this study, we leverage a convolutional neural network (CNN) architecture to directly pass data grids of various satellite data for the prediction of U.S. national ground-level NO2. We designed CNN architectures of various complexity which inputs both satellite and meteorological reanalysis data, testing both high and low resolution data grids. Our resulting model accurately predicted NO2 concentrations at both daily (R2 = 0.892, RMSE = 2.259, MAE = 1.534) and annual (R2 = 0.952, RMSE = 0.988, MAE = 0.690) temporal scales, with coarse resolution imagery and simple CNN architectures displaying the best and most efficient performance. Furthermore, the CNN outperforms traditional buffer distance models, including random forest (RF), feedforward neural network (FNN), and multivariate linear regression (MLR) approaches, resulting in the MLR performing the poorest at daily (R2 = 0.625, RMSE = 4.281, MAE = 3.102) and annual (R2 = 0.758, RMSE = 2.218, MAE = 1.652) scales. With the success of the CNN in this approach, satellite land use variables continue to be useful for the prediction of NO2. Using this computationally inexpensive model, we encourage the globalization of advanced LUR models as a low-cost alternative to traditional NO2 monitoring.

Anomalous Propagation and the Sinking of the Russian Warship Moskva

Abstract On 13 April 2022, the Russian warship Moskva was hit by two Ukrainian Neptune anti-ship missiles in the Black Sea, leading to its demise. Before launching an anti-ship missile, a target must first be detected and positioned, for example, by an accompanying radar system. However, when the missiles hit the Moskva she was well beyond the normal radar horizon of any ground-based radar system, making the ship undetectable under normal circumstances. Using meteorological reanalysis data, we show that at the time of the missile launch the prevailing weather conditions allowed a ground-based radar to detect targets far beyond the normal radar horizon through anomalous propagation conditions. During such conditions, the atmospheric index of refraction decreases rapidly with height, making electromagnetic radiation bend downward to, partly or fully, compensate the curvature of the Earth. The results show that atmospheric conditions must be considered carefully, even during warfare, as their impact on radar wave propagation can be considerable.

AI-Driven Identification of Contrail Sources: Integrating Satellite Observation and Air Traffic Data

Despite large uncertainties, it is now clear that condensation trails play a major role in aviation contribu-tion to climate change. In order to assess these uncertainties and reduce them, a database of observationsneeds to be built up to improve prediction models and to enable aircraft trajectories optimization basedon climate considerations. Detecting contrails in images is a time-consuming task without automation.In this paper, a dataset from GOES-16 satellite images is used to create a detection algorithm based onsegmentation methods. Then, a method is introduced for associating contrails with aircraft trajectoriesbased on ADS-B data. The Hough transform and meteorological forecast reanalysis data are applied tolink any contrail with a group of flights that may have contributed to its formation.

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations

This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012–2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 μg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

Ecosystem Evapotranspiration Partitioning and Its Spatial–Temporal Variation Based on Eddy Covariance Observation and Machine Learning Method

Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W/m2. The average annual T/ET of the examined ten ecosystems was 0.50 ± 0.08, with the highest value in deciduous broadleaf forests (0.68 ± 0.11), followed by mixed forests (0.61 ± 0.04), and the lowest in croplands (0.40 ± 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T/ET. Overall, at the interannual scale, LAI contributed 28% to the T/ET variation, while VPD had a small (5%) influence. On a seasonal scale, LAI also exerted a stronger impact (1~90%) on T/ET compared to VPD (1~77%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T/ET estimation.

Grid value of co-located offshore renewable energy

The paper investigates the added grid value of co-locating wave energy converters with offshore wind turbine generators and floating photovoltaic solar panels. Grid value is evaluated in terms of power generation variability and electrical infrastructure utilization. Using meteorological reanalysis data for the region of the North Sea, wave, solar and wind energy converters are modelled on an hourly basis to form the power profile of the joint operation. Necessary power ratings of the transmission system are provided both for stand-alone installations and the hybrid parks and is shown to be of higher utilization for the latter case. Suitable locations of hybrid parks are also analyzed and provided in terms of water depth, solar irradiation and favorable wave and wind climate. The joint operation is found to smoothen the power profile on all time scales but influences long term variations most heavily. Added grid value strongly depends on the park configuration as well as metocean conditions. It is shown that by co-locating wave, solar and wind energy converters the utilization of the electrical transmission system is increased compared to stand-alone installations.

Validation of the Atmospheric Dispersion Model NAME against Long-Range Tracer Release Experiments

Abstract The Met Office’s atmospheric dispersion model Numerical Atmospheric-Dispersion Modeling Environment (NAME) is validated against controlled tracer release experiments, considering the impact of the driving meteorological data and choices in the parameterization of unresolved motions. The Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from European Centre for Medium-Range Weather Forecasts (ECMWF) and data from the Advanced Research version of the Weather Research and Forecasting (WRF) Model. NAME predictions of air concentrations are assessed against the experimental measurements, using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parameterization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorological data.

Comprehensively exploring the characteristics and meteorological causes of ozone pollution events in Beijing during 2013–2020

Ozone (O3) pollution deteriorated in recent years in Beijing; however, the evolution characteristics and formation mechanism of O3 pollution events (OPEs) lack comprehensive exploration from a long-term perspective. Based on 35-site (Beijing) and multi-city (North China) O3 and meteorological monitoring and reanalysis data, totally 61 OPEs during 2013–2020 were counted in Beijing to reveal their characteristics, the relationship with regional pollution, and meteorological causes. Mild, moderate, and heavy pollution in all OPEs (203 ± 28 μg/m3 for averaged MDA8 O3) accounted for 68.4%, 27.8%, and 3.8% of 291 days, respectively. 78.7% of OPEs were concentrated in May–July. The averaged MDA8 O3 concentration, total days and durations of OPEs increased notably from 2013 to 2020, with a rate of 1.6 μg/m3/yr, 2.7 d/yr, and 0.2 d/yr, respectively. Overall, 90% (53%) of OPEs in Beijing co-occurred with regional pollution covering Beijing-Tianjin-Hebei (North China) under the predominant southerly airflow. Meteorological causes of different levels and spatial distribution of OPEs were explored through multi-scale weather processes. The increasing proportion of southwesterly-westerly synoptic circulation (49%–80%) and mountain-valley breeze (51%–70%), along with enhanced temperature, UV radiation, southerly wind, and temperature inversion and decreased relative humidity, collectively contributed to elevated levels of OPEs. The aforementioned meteorological condition facilitated O3 photochemical reactions, the transport of pollutants, and hindered diffusion. Three spatial types (urban, northern and southern suburban pollution) of OPEs were determined by the intensity and direction of southerly wind. Additionally, daytime O3 pollution elevated the mean peak O3 of nocturnal O3 enhancement events (NOEs) by 31 μg/m3 (41%) mainly induced by convective storms and low-level jets. Our comprehensive investigations of OPEs provide important scientific basis for the precise prediction and control of O3 pollution in Beijing.

Spatiotemporal Distribution Characteristics of Actual Evapotranspiration in the Qinghai–Tibet Plateau

Evapotranspiration is a key part of the water cycle between the atmosphere and the land surface, and it is an important parameter for studying the land–atmosphere system. Change and evolution have important implications. Therefore, the understanding and research of actual evapotranspiration (AET) can profoundly affect water use, ecological environment, temperature, and precipitation. In this paper, the single-layer monthly average reanalysis meteorological data of the Qinghai–Tibet Plateau from 1981 to 2020 was used to study and calculate the actual evapotranspiration in the Qinghai–Tibet Plateau, and the temporal and spatial variation characteristics, variation laws, and changes were analyzed by methods such as cumulative anomalies and the Mann–Kendall trend test. The results showed the following: (1) The evapotranspiration gradually decreased from southeast to northwest. The evapotranspiration in the southeastern region is strong, and the maximum value appears in the Hengduan Mountains. (2) The evapotranspiration was the largest in summer and gradually decreased from southeast to northwest; the evapotranspiration in spring and autumn was relatively uniform, with little overall difference, and the evapotranspiration was the lowest in winter. (3) There were mainly three spatial distribution modes of evapotranspiration in the Qinghai–Tibet Plateau, which were characterized by a significant and consistent change centered on the Tibet region, an east–west reverse type, and an east–west “negative-positive-negative” of the distributed three-pole space. The corresponding time coefficients characterized the interdecadal and interannual variation, and the decadal variation characteristics are more significant than the annual variation characteristics. (4) The actual evapotranspiration had step change; the step change years were 1989, 2002, 2011, and 2015, and there was an interval of about 5 years.

Meteorological-Data-Driven Rubber Tree Powdery Mildew Model and Its Application on Spatiotemporal Patterns: A Case Study of Hainan Island

Given that rubber is an important strategic material and the prevalence of rubber tree powdery mildew (RTPM) is a serious issue, the study of RTPM is becoming increasingly significant in aiding our understanding and managing rubber plantations. By enhancing our understanding, we may improve both the yield and quality of the rubber produced. Using meteorological station and reanalysis data, we employed factor expansion and three different feature-selection methods to screen for significant meteorological factors, ultimately constructing a data-driven RTPM disease index (RTPM-DI) model. This model was then used to analyze the spatiotemporal distribution of RTPM-DI in Hainan Island from 1980 to 2018, to reproduce and explore its patterns. The results show that (1) the RTPM-DI is dominantly negatively influenced by the average wind speed and positively affected by days with moderate rain; (2) the average wind speed and the days with moderate rain could explain 71% of the interannual variations in RTPM-DI, and a model established on the basis of these can simulate the changing RTPM-DI pattern very well (RMSE = 8.2511, MAE = 6.7765, MAPE = 0.2486, KGE = 0.9921, MSE = 68.081, RMSLE = 0.0953); (3) the model simulation revealed that during the period from 1980 to 2018, oscillating cold spots accounted for 72% of the whole area of Hainan Island, indicating a declining trend in RTPM-DI in the middle, western, southwestern, and northwestern regions. Conversely, new hot-spots and oscillating hot-spots accounted for 1% and 6% of the entire island, respectively, demonstrating an upward trend in the southeastern and northern regions. Additionally, no discernible pattern was observed for 21% of the island, encompassing the southern, eastern, and northeastern regions. It is evident that the whole island displayed significant spatial differences in the RTPM-DI pattern. The RTPM-DI model constructed in this study enhances our understanding of how climate change impacts RTPM, and it provides a useful tool for investigating the formation mechanism and control strategies of RTPM in greater depth.