Meteorological Applications最新文献

Nadimpalli, R., Y. S. Nekkali, K. K. Osuri, M. Mohapatra, D. Niyogi. 2025. “Understanding the Role of Antecedent Land Conditions on Rapid Intensity Changes in Landfalling Tropical Cyclones Over the Bay of Bengal.” Meteorological Applications 32, no. 6: e70134. https://doi.org/10.1002/met.70134.

In the published article, the funding details were missing. The following funding information should be included:

Funding: This work benefited in part from Monsoon Mission–III (IITM/MM-III/2023/IND-2/Sanction Order), NASA (80NSSC21K1008), NSF 2502272 and 241387, the UNESCO Chair, Farish Endownment and Oliver Fellowship at Jackson School of Geosciences, and the UT–UNESCO India International Initiative (U2I2 S. Kumar and R. Bashyam Gift).

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Soil moisture is classified as an essential climate variable (ECV) and is relevant to understanding hydrological, agricultural and ecological processes. Yet, in spite of its importance, direct observations of soil moisture remain limited globally—those that exist are typically limited in duration and spatial extent. Consequently, alternative approaches for estimating soil moisture have been developed, including water balance (‘bucket’) models, the use of remotely sensed information and the application of land surface modelling techniques. Spaceborne and land surface modelling based methods offer significant potential for monitoring and modelling soil moisture at a variety of spatial scales; however, their resolution remains relatively coarse for global and continental scale applications. At country scale, land surface models have demonstrated their potential but they require access to computational resources to deliver high resolution products. With the advent of machine- and deep- learning and data fusion techniques, high resolution global and regional soil moisture datasets are increasingly becoming available. Here, we evaluated a statistical machine learning approach to downscale the European Space Agency's (ESA) Climate Change Initiative (CCI) combined passive and active soil moisture product for Ireland using covariates that included both static (e.g., topography) and dynamic (e.g., gridded rainfall and temperature) variables. The model was developed using in situ cosmic ray neutron sensor (CRNS) measurements obtained from a network of sites in the United Kingdom, justified on the basis that the United Kingdom is geographically similar to Ireland in terms of its climate, soil types and land cover management practices. The model was found to perform reasonably well when validated against limited in situ data obtained from available time domain reflectometry (TDR) measurements available from Ireland. The developed model was subsequently used to derive spatial estimates of soil moisture on a 1 km grid across the Republic of Ireland.

Arid and Semi-Arid Lands have witnessed a surge in extreme climatic events with devastating environmental and livelihood effects. Understanding the dynamics of these extreme events, including drought, is essential for anticipatory action among resource-dependent communities. This study utilised Earth Observatory Systems and Google Earth Engine to analyse 24 years of Normalised Difference Drought Index trends in the Narok West landscape of Kenya across six timeframes (2000, 2005, 2010, 2015, 2020, and 2024). It revealed that the Normalised Difference Drought Index ranged from −0.489 (April 2000) to 0.469 (August 2005). Additionally, it established that during June–July–August dry seasons, there was an increase in the proportionate area under severe drought from 11% in 2000 to 24% in 2024 (average 19.17%, SD: 8.43%); and a decrease in the proportionate area under non-drought (good conditions) from 57.5% in 2000 to 40.5% in 2024 (average 40.5%, SD: 7.43%) respectively. Temporal increase in drought events was observed to be increasing from 2015, with extremes witnessed in 2020. Moreover, we established that season dry season rainfall averages 147.2 mm (95% CI: 100.7–193.8) and is decreasing at a rate of 1.25 mm annually. It is anticipated that the frequency and severity of drought across the landscape might increase due to weather variability, predominantly attributed to climate change. The increase could have a detrimental effect on water quality and quantity, public and ecosystem health, mental health and wellness, peace and protection, and rangeland ecology. Our study contributes to the body of research on future drought scenarios, which could assist with methodological and empirical studies and corrective actions. To adapt to and manage the effects of changing climate, these scenarios necessitate interdisciplinary community and landscape strategies, including the need for communities to develop a comprehensive understanding of the impacts of climate change and plan for the sustainable management of water resources.

Climate change is one of the biggest challenges of the 21st century. It severely affects many developing countries whose economy depends on climate-sensitive sectors with low adaptive capacity. Studies in northeastern Ethiopia have not addressed the future climate conditions well, using the recently released CMIP6 global climate models. This study focused on projections of precipitation and temperature changes and trends using CMIP6 GCMs in the eastern Amhara, Northeastern, Ethiopia. The gridded temperature and precipitation data were extracted from the Climatic Research Unit (CRU TS4.07) and Global Precipitation Climatology Centre (GPCCv2020) for 1984–2014, respectively. The historical and projected data were retrieved from the Earth Systems Grid Federation (ESGF). The projections were computed under SSP2-4.5 and SSP5-8.5 scenarios for two future periods: 2040s (2030–2060) and 2080s (2070–2100). The modified Mann–Kendall's test and Sen's slope were used to detect precipitation and temperature trends. The annual and seasonal projected precipitation and temperature results showed significant increasing trends at a 5% probability level. The annual precipitation will increase by 7.77% and 13.74% under the SSP2-4.5 scenario and by 14.02% and 28.48% under the SSP5-8.5 scenario for the 2040s and 2080s, respectively. The annual maximum temperature will increase by 0.92°C and 1.86°C under SSP2-4.5 and by 1.25°C and 3.39°C under the SSP5-8.5 scenario. Likewise, the annual minimum temperature will increase by 1.62°C and 1.97°C in the 2040s and by 2.56°C and 4.48°C in the 2080s under SSP2-4.5 and SSP5-8.5 scenarios, respectively. Regarding spatial distribution, the most significant precipitation and temperature changes are projected in the west and central parts of the study area. Increasing precipitation trends and temperature changes are projected under both scenarios and periods. Thus, an analysis of the impacts of climate change and the design of solutions would be very relevant.

Chongqing is one of the regions in China most frequently and severely affected by potato late blight (PLB), which is a fungal disease caused by phytophthora infestans (PI). To improve PLB occurrence forecast skills in this area, a 1–10 day forecast system (PLBOFS-CQ) based on the CARAH model and the intelligent grid forecast (IGF) of air temperature (AT) and relative humidity (RH) issued by the Chongqing Meteorological Observatory has been developed, showing certain forecast skill. However, IGF errors inevitably exist and increase with forecast lead time, limiting the forecast accuracy. To address this issue, this study investigated an ensemble forecast method for PLB occurrence based on the CARAH model. First, error distribution characteristics of IGF were analyzed, providing a comprehensive understanding of the related forecast uncertainties. On this basis, an error variance–dependent random perturbation method has been developed to generate 200-member IGF ensembles. Long-term verification showed that this perturbation method is reasonable and applicable. Building on this, ensemble mean forecasts (EMF), ensemble quantile forecasts (EQF), and ensemble probability forecasts (EPF) for PI infection have been developed and tested. Among these, maximum EQFs performed best, significantly outperforming the control forecast. The averaged threat score (TS) for infection timing improved by 92.7% at 1–3 day and 34.6% at 4–10 day lead times, whereas improvements for the timing when the Conce score reached 4 after infection were 220.3% and 63.8%, respectively. EPF also demonstrated useful skill, with probabilistic forecasts providing practical references for users. Future work will focus on extending applications and developing an operational ensemble forecast system for PLB occurrence in Chongqing. More broadly, this work demonstrates the potential of ensemble forecast method in agricultural meteorology and provides a pathway for advancing disease forecasting and management in other crop systems.

Caron, J.-F. and B. Casati. 2025. “On the Reliability of Surface Observations and the Pitfalls of Verification Against Own Analyses.” Meteorological Applications 32, no. 6: e70129. https://doi.org/10.1002/met.70129.

The article by Bélair et al. (2003) is cited solely in Section 2.

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Accurate forecasting of daily precipitation is critical for agricultural planning and effective water resource management. This study evaluates the capability of machine learning (ML) and deep learning (DL) models to predict daily precipitation using 40 years (1983–2023) of data from five synoptic stations in western Iran. Seven models were tested: Multiple Linear Regression (MLR), Polynomial Regression (PR), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Q-learning with Long Short-Term Memory (DQN-LSTM). Each model was trained on 10-day input sequences to predict precipitation with a one-day lead time, capturing short-term temporal dependencies. Model performance, assessed using R² and RMSE, varied across stations, with DQN-LSTM achieving the best results, explaining over 84% of daily precipitation variability and yielding the lowest RMSE values. Although PR, RF, and XGBoost provided reasonable accuracy, DT and SVR underperformed. However, it is important to note that the models that achieved the best RMSE and R² may not necessarily perform as well in predicting maximum precipitation values at stations. In general, all forecasting methods tend to underestimate the R95p index across stations. Nevertheless, the DQN-LSTM model demonstrates superior overall skill in predicting extreme precipitation indices such as R95p and RX1day. However, for the frequency of extreme precipitation days, the predictions from PR, DT, RF, and XGBoost exhibit closer agreement with the observed values. These findings demonstrate the potential of hybrid DL models like DQN-LSTM to improve both overall forecast accuracy and extreme event prediction, providing valuable insights for water management and disaster mitigation in regions with variable climates such as western Iran.

Two tropical cyclones (TCs)—Phailin and Lehar over the Bay of Bengal (BoB) in 2013 exhibit contrasting rapid intensity changes near landfall, despite forming in similar synoptic environments. Phailin underwent a rapid intensification of ~70 knots between 10 and 11 October 2013, while Lehar rapidly weakened by 30 knots between 27 and 28 November 2013. This study investigates the effects of environmental factors such as vertical wind shear (VWS), the intrusion of cold/dry air, and antecedent land surface conditions (soil moisture and soil temperature; SM/ST) using the cloud-resolving configuration of the Hurricane Weather Research and Forecasting (HWRF) model (at 27/9/and 3-km resolutions). Phailin was characterized by a robust vortex that resisted disruption due to low VWS (10 knots) and modulated its surrounding environment. Whereas Lehar encountered a tilted vortex due to significant VWS (20 knots) and intrusion of mid-level cold, dry air linked to a nearby subtropical high, which weakened its convection/intensity. Cold, dry air alone had a limited impact on storm structure unless accompanied by VWS, which allowed environmental influences to penetrate the core. To quantify the influence of SM/ST, a series of sensitivity experiments were conducted by interchanging them between the two cyclones under similar synoptic backgrounds. Substituting Lehar's land surface conditions into Phailin's simulation showed minimal impact on Phailin's peak intensity, while altering Lehar's surface variables delayed its rapid weakening by 24 h and advanced landfall by 6 h. The study highlights that antecedent land conditions significantly affect storm characteristics even when interacting with land before landfall, highlighting the importance of accurate land surface initialization for intensity forecasts.

Ensembles provide a wealth of information to aid forecasters in their day-to-day operations, but with increasing ensemble size and complexity, there is rarely time to fully interrogate their outputs. Clustering ensemble members into distinct scenarios based on the co-location of hazardous weather features has previously shown promise when applied to global ensemble outputs. However, it is currently unclear whether further value can be gained when applying clustering to convection-permitting ensemble (CPE) outputs. This study compares precipitation clusters between the operational MOGREPS-G driving ensemble and the nested MOGREPS-UK CPE run at the (UK) Met Office during summer 2023. When applied over the UK domain, CPE clustering does not provide clear value compared to global ensemble clustering. Instead, clusters become increasingly similar with leadtime, strongly indicating that CPE clusters are most sensitive to the synoptic forcing common between the two ensembles and that the presence of convective-scale detail has little influence. However, when focussed on a region impacted by hazardous convection, CPE clustering identified distinct precipitation scenarios and provided improved probabilistic value compared to driving-ensemble clustering. Finally, by comparing clusters with radar observations, it is demonstrated that the fraction of members supporting a particular scenario is a reliable quantitative prediction of the probability that the given scenario will be the most accurate. We recommend that global ensemble clustering is sufficient over larger domains, while CPE clustering is most useful when applied at regional scales.

Meteorological station networks are often not homogeneously distributed across geographical verification domains, and usually unpopulated regions (such as deserts or forested regions) are less observed than densely populated regions (such as agricultural regions or cities). Therefore, spatially aggregated verification scores evaluated against station measurements are often dominated by the forecast performance in the regions with a denser observation network. In this study, we explore some solutions used in operational practices for reducing the effects of station network geographical inhomogeneity on spatially aggregated verification scores. The effects of network inhomogeneities on aggregated verification scores is first illustrated over Canada and high latitudes. Thinning the verifying observations to a less dense yet spatially homogeneous network (e.g., considering one station every 1° × 1° latitude–longitude sector) addresses the inhomogeneity issue, but not optimally, since it impoverishes the verification sample. In order to fully exploit the observation network, scores are spatially aggregated by applying a weight to each station, where the weights are inversely proportional to the network density around the station. The weights are evaluated by a Gaussian kernel: we describe a methodology and provide the optimal influence radius, evaluated for the SYNOP station network for different regions around the globe. We conclude that the Gaussian weighting provides more reliable results than thinning, and more representative results than considering the whole (inhomogeneous) station network.