Meteorological Applications最新文献

{"title":"Performance variations of global precipitation products in detecting drought episodes in three wet seasons in Ethiopia: Part II—statistical analysis","authors":"Mekonnen Adnew Degefu,&nbsp;Woldeamlak Bewket","doi":"10.1002/met.2154","DOIUrl":"10.1002/met.2154","url":null,"abstract":"<p>The need to evaluate global climate data has increased in recent times. In this study, we evaluate the ability of global precipitation products to monitor drought during three wet seasons (<i>Belg/</i>March–May, <i>Kiremt</i>/June–September and <i>Autumn</i>/September–November) and associated rainfall regions in Ethiopia. We employed statistical methods to quantify and evaluate precipitation products based on probability of drought detection (POD), the extent of false alarms (FAR) and the critical success index (CSI) to see the overall performance of the studied precipitation products. The majority of the studied precipitation datasets were relatively better in capturing the <i>Autumn</i> drought in southern Ethiopia, and 18 out of 21 precipitation products captured accurately more than 50% of observed droughts. The CSI scores for this season are also above 0.5 for 14 precipitation products. On the other hand, 15 and 14 precipitation products accurately captured more than 50% of the seasonal drought in <i>Kiremt</i> and <i>Belg</i> rainfall seasons in north-eastern Ethiopia. In contrast, most precipitation products do not clearly represent the drought conditions of the <i>Kiremt</i> season in north-western Ethiopia. Only 8 of the 21 precipitation products accurately captured more than 50% of the observed drought in this region, and only 6 precipitation products had a CSI score greater than 0.5. The results can facilitate the selection of precipitation products for drought monitoring purposes, for use in specific wet seasons and regions of Ethiopia.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.2154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135736763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modification of standardized precipitation index in different climates of Iran","authors":"Mehdi Nadi,&nbsp;Saeid Shiukhy Soqanloo","doi":"10.1002/met.2155","DOIUrl":"10.1002/met.2155","url":null,"abstract":"<p>The most popular metric for drought monitoring is the standardized precipitation index (SPI). It does have some drawbacks, though, such as using gamma probability distribution function as the default distribution and not taking into account seasonal variations. The effectiveness of SPI in monitoring droughts in various Iranian climates was examined in this study, and in order to address its limitations, it was contrasted with the modified SPI (mod-SPI). Then, the performance of the mod-SPI and SPI in drought monitoring was compared with nine meteorological stations throughout Iran during the period 1956–2010. The results showed that the gamma distribution function was not selected in any of the 12 timescales (TS) considered at the studied meteorological stations. Based on the Cohen's kappa index analysis, there is a clear difference between the SPI and mod-SPI in monitoring drought classes, which is more evident at low TS (less than 6 months), but at much higher TS (above TS-9), the two indices almost coincide. The results showed that in the arid and semiarid areas, the SPI and mod-SPI are significantly different, so it seems that the SPI monitors aridity rather than drought. The results imply that using mod-SPI instead of SPI provides more accurate drought monitoring by eliminating the seasonal effects of precipitation data. Considering the low efficiency of SPI in short-term drought monitoring (less than 9 months) in arid and semiarid regions, it is recommended to use mod-SPI instead of SPI in most parts of Iran, especially on the southern coasts of Iran.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.2155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135737004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of climatic conditions on Normalized Difference Vegetation Index variability in forest in Poland (2002–2021)","authors":"Kinga Kulesza,&nbsp;Agata Hościło","doi":"10.1002/met.2156","DOIUrl":"10.1002/met.2156","url":null,"abstract":"<p>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.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.2156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135736358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison between statistical and dynamical downscaling of rainfall over the Gwadar-Ormara basin, Pakistan","authors":"Raazia Attique,&nbsp;Tom Rientjes,&nbsp;Martijn Booij","doi":"10.1002/met.2151","DOIUrl":"10.1002/met.2151","url":null,"abstract":"<p>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 <i>R</i>² 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.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.2151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the suitability of Marginal Distribution Sampling as a gap-filling method using some meteorological data from seven sites in West Africa","authors":"Djidjoho Renaud Roméo Koukoui,&nbsp;Ossénatou Mamadou,&nbsp;Miriam Hounsinou,&nbsp;Basile Kounouhéwa","doi":"10.1002/met.2152","DOIUrl":"https://doi.org/10.1002/met.2152","url":null,"abstract":"<p>Meteorological data are useful in many fields related to climate change studies and their use often requires them to be continuous. To date, marginal distribution sampling (MDS), which consists of filling a missing value with an average of the data that are found in similar meteorological conditions over a flexible time window, is widely adopted in the FLUXNET community. In this work, we evaluate the performance of MDS at diurnal and monthly scales for the incoming shortwave radiation (Swin), relative humidity (RH), vapour pressure deficit (VPD), air and soil temperatures (Tair, Tsoil) acquired across seven sites in West Africa. The criteria tested are the MDS's ability to (i) fill gaps while reducing the error rate, (ii) represent proper variability within data and finally (iii) ensure homogeneity between its output and original data. We found during the daytime that MDS is adequate for filling gaps in Swin when both reducing error rate and a good representation of variability are targeted. If the goal is to have a small error rate, then this approach is recommended for all investigated variables except VPD. During nighttime, MDS is satisfactory to minimize the error when filling gaps in Tair, Tsoil and RH while to represent their variabilities it becomes more sensitive to the rate of missing data. At a monthly scale, the gap-filled data are consistent with the original ones for all variables attributable to data size and a wider sliding window that allows more data under similar conditions to be considered.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.2152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50140292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verification of satellite and model products against a dense rain gauge network for a severe flooding event in Kumasi, Ghana","authors":"Jacob Agyekum,&nbsp;Leonard K. Amekudzi,&nbsp;Thorwald Stein,&nbsp;Jeffrey N. A. Aryee,&nbsp;Winifred Ayinpogbilla Atiah,&nbsp;Elijah Adesanya Adefisan,&nbsp;Sylvester K. Danuor","doi":"10.1002/met.2150","DOIUrl":"https://doi.org/10.1002/met.2150","url":null,"abstract":"<p>Floods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.2150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50145257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting agricultural drought risks: A case study of the rice crop (Oryza sativa) and the TAMSAT-ALERT system in Guyana","authors":"Donessa L. David","doi":"10.1002/met.2149","DOIUrl":"https://doi.org/10.1002/met.2149","url":null,"abstract":"<p>Drought-related risks pose a threat to the agricultural sector of Guyana despite the country's wealth of freshwater resources. As a result, the advancement of the understanding of soil moisture deficits as a means of forecasting agricultural drought is needed to aid farmers, extension officers, and other agricultural decision-makers. Hence, this study has been motivated by the following research question: Can the Tropical Applications of Meteorology using SATellite data—AgriculturaL Early waRning sysTem (TAMSAT-ALERT) be used to assess the meteorological risk to cultivation at key points in the growing season? Due to the absence of in situ soil moisture data for the area of study, the Joint UK Land and Environment Simulator (JULES) model was used to model the historical soil moisture, based on gauge precipitation data and NCEP reanalysis data. A case study approach during the 1997 growing seasons of the rice crop was taken to determine whether the TAMSAT-ALERT can be used to detect drought-related risks during the growing season of the crop. Additionally, the skill of the TAMSAT-ALERT drought forecasting system was highly dependent on the land surface state at the initialization of the forecast period. Therefore, the meteorological conditions over the area of interest mainly precipitation in the months or weeks leading up to the initialization of the forecast will have a strong influence on the soil moisture at that period.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.2149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50143649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding your audience: The influence of social media user-type on informational behaviors and hazard adjustments during Hurricane Dorian","authors":"Amber Silver,&nbsp;Brandon Behlendorf","doi":"10.1002/met.2148","DOIUrl":"https://doi.org/10.1002/met.2148","url":null,"abstract":"<p>In 2019, Hurricane Dorian affected Atlantic Canada with widespread impacts across the region. In the days preceding landfall, there was a great deal of discussion about the storm and its potential impacts. This discussion also extended onto Twitter, which provided a platform for users to engage with storm-related information. In this research, we disseminated a questionnaire to residents of Atlantic Canada from late September to late October through <i>Qualtrics</i>, an online survey provider. The questionnaire explored how Twitter influenced respondents' (<i>n</i> = 1218) self-reported informational behaviors (i.e., searching, sharing, and processing) and behavioral responses before, during, and after the storm. The results demonstrate that users' informational needs and preferences were closely related to their online behaviors. For example, conduits (i.e., those who both searched for and shared information) were highly proactive users who disseminated information about evacuations, recommended protective actions, and other official guidance more so than others. Conduits were also the most likely to heed official guidance in terms of their own preparedness and response. Amplifiers (i.e., those who only share information) and consumers (i.e., those who only search for information) were also motivated to take action by information they saw online, albeit at lower rates than conduits. Lastly, the results demonstrate that users can be positively influenced by information they see online even if they do not actively engage with it. Taken together, the results of this study suggest that Twitter users may interact with storm-related information in more nuanced and complex ways than previously understood.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.2148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China","authors":"Dong Wang,&nbsp;Jiahong Liu,&nbsp;Qinghua Luan,&nbsp;Weiwei Shao,&nbsp;Xiaoran Fu,&nbsp;Hao Wang,&nbsp;Yanling Gu","doi":"10.1002/met.2144","DOIUrl":"https://doi.org/10.1002/met.2144","url":null,"abstract":"<p>Projecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K-nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near-term), 2043–2062 (Mid-term), and 2081–2100 (Long-term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5-8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2-4.5 with −30.48% to 44.67%, and the smallest under SSP1-2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long-term, followed by Mid-term, and the smallest in the Near-term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.2144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate zones in Iran","authors":"Mohammad Saeed Najafi,&nbsp;Omid Alizadeh","doi":"10.1002/met.2147","DOIUrl":"https://doi.org/10.1002/met.2147","url":null,"abstract":"<p>Climate classification provides a framework for a better understanding of the dominant weather patterns in different regions of the Earth. This study aims at identifying climate zones in Iran based on the analysis of monthly temperature and precipitation over 139 synoptic stations across Iran during the period 1991–2020. Based on the application of the principal component analysis, we identified six distinct climate zones in Iran: mild and humid, cool and sub-humid, cold and temperate semi-arid, warm and semi-arid, cool and arid, and warm and hyperarid. The highest precipitation occurs in the southern coastal plains of the Caspian Sea, characterized by a mild and humid climate. The climate of western Iran is identified as cool and sub-humid, while northwestern Iran is characterized by a cold and temperate semi-arid climate. Southwestern Iran is identified as a region with a warm and semi-arid climate, while northeastern Iran has a cool and arid climate. Southeastern and central Iran are both characterized by a warm and hyperarid climate. The highest monthly and seasonal precipitation values over Iran occur in March (48.6 mm) and winter (134.2 mm), respectively, while the highest monthly and seasonal mean temperature values occur in July (29.1°C) and summer (28.0°C), respectively. In terms of seasonal variation, the maximum precipitation occurs in the southern coastal plains of the Caspian Sea in autumn, while the minimum occurs in southwestern Iran in summer. Our results have important implications for better understanding and analysing the climatic characteristics across Iran.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"30 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.2147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50130353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}