A. Behrangi, Yang Song, G. Huffman, Robert F. Adler
Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understand how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have higher precipitation rate than the previous version, except for the radar-only product. Within ~65°S-N, covered by all of the instruments, this increase ranges from about 9% for the combined radar-radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics, (25°S-25°N) passive microwave sounders show the highest precipitation rate among all of the precipitation products studied, and the highest increase (~19%) compared to their previous version. Precipitation products are least consistent in mid-latitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.
{"title":"Comparative Analysis of the Latest Global Oceanic Precipitation Estimates from GPM V07 and GPCP V3.2 Products","authors":"A. Behrangi, Yang Song, G. Huffman, Robert F. Adler","doi":"10.1175/jhm-d-23-0082.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0082.1","url":null,"abstract":"Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understand how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have higher precipitation rate than the previous version, except for the radar-only product. Within ~65°S-N, covered by all of the instruments, this increase ranges from about 9% for the combined radar-radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics, (25°S-25°N) passive microwave sounders show the highest precipitation rate among all of the precipitation products studied, and the highest increase (~19%) compared to their previous version. Precipitation products are least consistent in mid-latitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"35 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel C. Watters, Patrick N. Gatlin, D. Bolvin, G. Huffman, Robert Joyce, P. Kirstetter, E. Nelkin, S. Ringerud, J. Tan, Jianxin Wang, David Wolff
NASA’s multi-satellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers.
{"title":"Oceanic Validation of IMERG-GMI Version 6 Precipitation using the GPM Validation Network","authors":"Daniel C. Watters, Patrick N. Gatlin, D. Bolvin, G. Huffman, Robert Joyce, P. Kirstetter, E. Nelkin, S. Ringerud, J. Tan, Jianxin Wang, David Wolff","doi":"10.1175/jhm-d-23-0134.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0134.1","url":null,"abstract":"NASA’s multi-satellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"128 8","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eunkyo Seo, Paul A. Dirmeyer, Michael Barlage, Heiln Wei, Michael Ek
Abstract This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–2017 in regard to coupled land-atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land-atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model’s ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land-atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal timescales.
{"title":"Evaluation of land-atmosphere coupling processes and climatological bias in the UFS global coupled model","authors":"Eunkyo Seo, Paul A. Dirmeyer, Michael Barlage, Heiln Wei, Michael Ek","doi":"10.1175/jhm-d-23-0097.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0097.1","url":null,"abstract":"Abstract This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–2017 in regard to coupled land-atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land-atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model’s ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land-atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal timescales.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns which characterize these cold regions. In this study, we used datasets from 12 climate simulations associated to seven global climate models and four future scenarios and participating in the Coupled Model Intercomparison Project Phase 6, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series, to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework with the Alberta oil sands region in Canada, to support the monitoring of environmental changes in this region, relative to the established baseline 1985-2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the Standardized Precipitation Evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study, suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.
{"title":"Temporal assessment of GCM-driven hydroclimatic conditions for the Alberta oil sands region, Canada","authors":"Alida Thiombiano, Alain Pietroniro, Tricia Stadnyk, Hyung Eum, Babak Farjad, Anil Gupta, Barrie Bonsal","doi":"10.1175/jhm-d-23-0051.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0051.1","url":null,"abstract":"Abstract Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns which characterize these cold regions. In this study, we used datasets from 12 climate simulations associated to seven global climate models and four future scenarios and participating in the Coupled Model Intercomparison Project Phase 6, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series, to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework with the Alberta oil sands region in Canada, to support the monitoring of environmental changes in this region, relative to the established baseline 1985-2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the Standardized Precipitation Evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study, suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"40 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jian Li, Rucong Yu, Xiaoyuan Yue, Mingming Zhang, Nina Li
Abstract Spatial unevenness, especially local unevenness, is a key characteristic of precipitation and has the potential to be a metric to gauge the performance of high-resolution models. In this paper, a local unevenness index (LUI) is proposed to quantify the heterogeneity of precipitation in central and eastern China. The local unevenness of precipitation is dominantly influenced by local topography, and high LUIs spatially correspond to high local terrain relief. Along 30°N, the correlation coefficient between the LUI and local relief reaches 0.893. Stations with large enhancement and steep gradients in precipitation are identified as local maximum (LM) stations. According to the distinct impacts of various scales of terrain, all 59 LM stations are categorized into three groups: the high-elevation group, the edge group, and the eastern isolated-mountain group. The three groups present different distributions of precipitation with altitude: a double-peak pattern in the high-elevation group, a low-altitude peak in the edge group, and a high-altitude peak in the eastern isolated-mountain group. The seasonal variations in all groups are characterized by relatively more precipitation occurring at higher (lower) elevations in the warm (cold) season. The high-elevation group shows a general increasing (decreasing) frequency tendency with altitude in the warm (cold) season. The low-altitude (high-altitude) frequency peak in the edge (eastern isolated-mountain) group is more prominent in the cold (warm) season. The LUI and LM can be used as straightforward and quantified metrics to measure the performance of high-resolution models in reproducing the local-scale features of precipitation and their seasonal variations. Significance Statement The purpose of this study is to promote the knowledge of precipitation unevenness on a local scale. This is important to hydrological processes and water resource management. Our results provide LUI and LM to quantify the spatial unevenness of precipitation on a local scale and further analyze the climatic characteristics and the seasonal variations of precipitation unevenness over central and eastern China. These metrics can be used as quantitative criteria to evaluate the performance of high-resolution models.
{"title":"Quantifying the spatial unevenness of precipitation in central and eastern China","authors":"Jian Li, Rucong Yu, Xiaoyuan Yue, Mingming Zhang, Nina Li","doi":"10.1175/jhm-d-22-0240.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0240.1","url":null,"abstract":"Abstract Spatial unevenness, especially local unevenness, is a key characteristic of precipitation and has the potential to be a metric to gauge the performance of high-resolution models. In this paper, a local unevenness index (LUI) is proposed to quantify the heterogeneity of precipitation in central and eastern China. The local unevenness of precipitation is dominantly influenced by local topography, and high LUIs spatially correspond to high local terrain relief. Along 30°N, the correlation coefficient between the LUI and local relief reaches 0.893. Stations with large enhancement and steep gradients in precipitation are identified as local maximum (LM) stations. According to the distinct impacts of various scales of terrain, all 59 LM stations are categorized into three groups: the high-elevation group, the edge group, and the eastern isolated-mountain group. The three groups present different distributions of precipitation with altitude: a double-peak pattern in the high-elevation group, a low-altitude peak in the edge group, and a high-altitude peak in the eastern isolated-mountain group. The seasonal variations in all groups are characterized by relatively more precipitation occurring at higher (lower) elevations in the warm (cold) season. The high-elevation group shows a general increasing (decreasing) frequency tendency with altitude in the warm (cold) season. The low-altitude (high-altitude) frequency peak in the edge (eastern isolated-mountain) group is more prominent in the cold (warm) season. The LUI and LM can be used as straightforward and quantified metrics to measure the performance of high-resolution models in reproducing the local-scale features of precipitation and their seasonal variations. Significance Statement The purpose of this study is to promote the knowledge of precipitation unevenness on a local scale. This is important to hydrological processes and water resource management. Our results provide LUI and LM to quantify the spatial unevenness of precipitation on a local scale and further analyze the climatic characteristics and the seasonal variations of precipitation unevenness over central and eastern China. These metrics can be used as quantitative criteria to evaluate the performance of high-resolution models.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"293 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land–atmosphere coupling—surface flux equilibrium (SFE) theory—to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century. Significance Statement Irrigation grew profoundly in the United States over the twentieth century, increasing the resilience of American agriculture to drought. Yet observational records of agricultural drought, and its response to irrigation, are limited to the satellite era. Here, we show that a common measure of agricultural drought (the evaporative fraction, EF) can be estimated using widespread weather data, extending the agricultural drought record decades further back in time. We show that EF estimated using our approach is both sensitive and specific to the occurrence of irrigation, unlike an alternative derived from a reanalysis.
{"title":"An Observational, Irrigation-Sensitive Agricultural Drought Record from Weather Data","authors":"Lois I. Tang, Kaighin A. McColl","doi":"10.1175/jhm-d-23-0026.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0026.1","url":null,"abstract":"Abstract The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land–atmosphere coupling—surface flux equilibrium (SFE) theory—to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century. Significance Statement Irrigation grew profoundly in the United States over the twentieth century, increasing the resilience of American agriculture to drought. Yet observational records of agricultural drought, and its response to irrigation, are limited to the satellite era. Here, we show that a common measure of agricultural drought (the evaporative fraction, EF) can be estimated using widespread weather data, extending the agricultural drought record decades further back in time. We show that EF estimated using our approach is both sensitive and specific to the occurrence of irrigation, unlike an alternative derived from a reanalysis.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"66 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135320488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract World extremes in meteorology are important as they can be used as indicators for climate change. This was one of the main reasons for the creation of the World Meteorological Organization’s World Weather and Climate Extremes Archive in 2006. In contrast to temperature, for instance, which can be described by a single parameter, point rainfall must be described by two parameters, for example, precipitation depth and duration. This makes it difficult to directly compare different rainfall records. In this article, however, it is shown that the world’s greatest rainfall events, with durations ranging from 1 min to 2 years, all have nearly the same precipitation intensity duration index, a new dimensionless number. As a theoretical consequence, the intensity of all these record rainfalls is inversely proportional to the square root of their duration. This physically based result is consistent with earlier statistically based findings. The last measured record rainfall on the World Meteorological Organization’s record list is the point rainfall with the largest precipitation intensity duration index since 1860. This 4-day rainfall that began on 24 February 2007 on Cratère Commerson, Réunion Island, can be considered the largest point rainfall within documented records. Significance Statement Floods resulting from extreme rainstorms can be very costly and deadly; thus, understanding such extreme events is very important. Knowledge of extreme rainstorms is also important in determining how much and how fast our climate is changing. In this article, a new dimensionless number, the precipitation intensity duration index (PID) is presented. The world’s greatest point rainfall events, with durations ranging from 1 min to 2 years, all have nearly the same PID. One rainfall event, however, has a considerably larger PID than all others, namely, a 4-day rainfall that began on 24 February 2007 on Cratère Commerson, Réunion Island. Therefore, this rainfall can be considered the largest point rainfall within documented records.
世界极端天气在气象学中很重要，因为它们可以作为气候变化的指标。这是世界气象组织在2006年创建世界极端天气和气候档案的主要原因之一。例如，温度可以用一个参数来描述，而点降雨必须用两个参数来描述，例如降水深度和持续时间。这使得直接比较不同的降雨记录变得困难。然而，本文表明，世界上最大的降水事件，持续时间从1分钟到2年，都具有几乎相同的降水强度持续时间指数，这是一个新的无因次数。作为一个理论上的结果，所有这些记录降雨的强度与持续时间的平方根成反比。这一基于物理的结果与早期基于统计的发现是一致的。世界气象组织记录清单上最后一次测量的雨量记录是1860年以来降水强度持续指数最大的点雨量。2007年2月24日开始于r联合岛crat re Commerson的4天降雨可以被认为是有记录以来最大的点降雨。极端暴雨引起的洪水可能会造成巨大的损失和致命的后果;因此，了解这种极端事件是非常重要的。对极端暴雨的了解对于确定气候变化的程度和速度也很重要。本文提出了一种新的无量纲数——降水强度持续指数(PID)。世界上最大的点降雨事件，持续时间从1分钟到2年不等，都具有几乎相同的PID。然而，有一次降雨事件的PID比其他所有事件都大得多，即2007年2月24日开始在r联合岛crat re Commerson发生的为期4天的降雨。因此，这个降雨量可以被认为是有记录的记录中最大的点降雨量。
{"title":"The World’s Largest Point Rainfall Found Using the Precipitation Intensity Duration Index","authors":"Rasmus Wiuff","doi":"10.1175/jhm-d-23-0012.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0012.1","url":null,"abstract":"Abstract World extremes in meteorology are important as they can be used as indicators for climate change. This was one of the main reasons for the creation of the World Meteorological Organization’s World Weather and Climate Extremes Archive in 2006. In contrast to temperature, for instance, which can be described by a single parameter, point rainfall must be described by two parameters, for example, precipitation depth and duration. This makes it difficult to directly compare different rainfall records. In this article, however, it is shown that the world’s greatest rainfall events, with durations ranging from 1 min to 2 years, all have nearly the same precipitation intensity duration index, a new dimensionless number. As a theoretical consequence, the intensity of all these record rainfalls is inversely proportional to the square root of their duration. This physically based result is consistent with earlier statistically based findings. The last measured record rainfall on the World Meteorological Organization’s record list is the point rainfall with the largest precipitation intensity duration index since 1860. This 4-day rainfall that began on 24 February 2007 on Cratère Commerson, Réunion Island, can be considered the largest point rainfall within documented records. Significance Statement Floods resulting from extreme rainstorms can be very costly and deadly; thus, understanding such extreme events is very important. Knowledge of extreme rainstorms is also important in determining how much and how fast our climate is changing. In this article, a new dimensionless number, the precipitation intensity duration index (PID) is presented. The world’s greatest point rainfall events, with durations ranging from 1 min to 2 years, all have nearly the same PID. One rainfall event, however, has a considerably larger PID than all others, namely, a 4-day rainfall that began on 24 February 2007 on Cratère Commerson, Réunion Island. Therefore, this rainfall can be considered the largest point rainfall within documented records.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"35 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this study, a multilevel temporal convolutional network (MTCN) model is proposed for 1-month-ahead forecasting of precipitation. In the MTCN model, à trous wavelet transform (ATWT) is first utilized to decompose the standardized monthly precipitation anomaly and its candidate predictors into their components with the different time scales. Then, at each of the time levels, a temporal convolutional network (TCN) model is built to forecast the precipitation anomaly component by combining with the Boruta selection algorithm (TCN-B) for identifying important model inputs from corresponding predictor components. Finally, the precipitation forecast is achieved by summing all the forecasted anomaly components and applying the inverse transform of the standardized monthly precipitation. The proposed MTCN is tested and compared to the TCN-B and TCN using monthly precipitation at 189 stations in the Yangtze River basin. The TCN-B is formed by coupling the TCN with the Boruta algorithm. The comparison results show that the TCN-B outperforms the TCN, and the MTCN has the best performance among the three models. Compared to the TCN, the MTCN provides a significant improvement for all stations, especially for the eastern stations of the basin. It is also shown that all three models perform better in spring and summer and have the weakest abilities in winter. The MTCN has a great improvement in predicting precipitation of all four seasons compared with the other two models. Additionally, all three models exhibit better prediction performance in the western region than in the eastern region of the basin, which is strongly related to the spatial distribution of precipitation variability.
{"title":"A Multilevel Temporal Convolutional Network Model with Wavelet Decomposition and Boruta Selection for Forecasting Monthly Precipitation","authors":"Lizhi Tao, Xinguang He, Jiajia Li, Dong Yang","doi":"10.1175/jhm-d-22-0177.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0177.1","url":null,"abstract":"Abstract In this study, a multilevel temporal convolutional network (MTCN) model is proposed for 1-month-ahead forecasting of precipitation. In the MTCN model, à trous wavelet transform (ATWT) is first utilized to decompose the standardized monthly precipitation anomaly and its candidate predictors into their components with the different time scales. Then, at each of the time levels, a temporal convolutional network (TCN) model is built to forecast the precipitation anomaly component by combining with the Boruta selection algorithm (TCN-B) for identifying important model inputs from corresponding predictor components. Finally, the precipitation forecast is achieved by summing all the forecasted anomaly components and applying the inverse transform of the standardized monthly precipitation. The proposed MTCN is tested and compared to the TCN-B and TCN using monthly precipitation at 189 stations in the Yangtze River basin. The TCN-B is formed by coupling the TCN with the Boruta algorithm. The comparison results show that the TCN-B outperforms the TCN, and the MTCN has the best performance among the three models. Compared to the TCN, the MTCN provides a significant improvement for all stations, especially for the eastern stations of the basin. It is also shown that all three models perform better in spring and summer and have the weakest abilities in winter. The MTCN has a great improvement in predicting precipitation of all four seasons compared with the other two models. Additionally, all three models exhibit better prediction performance in the western region than in the eastern region of the basin, which is strongly related to the spatial distribution of precipitation variability.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"2 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative precipitation predictions over 1–30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7–0.8 after 3–5 days, then gradually decreases and settles at approximately 0.2–0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model’s prediction is 0.2–0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables. Significance Statement The daily and cumulative precipitation prediction skills of three subseasonal prediction products were evaluated over China in this study. Our results reveal the spatiotemporal variations in prediction skill, especially with respect to time scale. Compared to daily precipitation predictions, cumulative precipitation predictions are more skillful, with correlation coefficients peaking at 0.7–0.8 after 3–5 days. These results can provide valuable information for water resource managers who are more concerned with the general conditions over a period than with hydrometeorological events occurring on a particular day. This study can guide end users in applying appropriate time scales to fully exploit numerical weather prediction information and satisfy their specific needs.
{"title":"Spatiotemporal Variations in Precipitation Forecasting Skill of Three Global Subseasonal Prediction Products over China","authors":"Shiyuan Liu, Wentao Li, Qingyun Duan","doi":"10.1175/jhm-d-23-0071.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0071.1","url":null,"abstract":"Abstract Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative precipitation predictions over 1–30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7–0.8 after 3–5 days, then gradually decreases and settles at approximately 0.2–0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model’s prediction is 0.2–0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables. Significance Statement The daily and cumulative precipitation prediction skills of three subseasonal prediction products were evaluated over China in this study. Our results reveal the spatiotemporal variations in prediction skill, especially with respect to time scale. Compared to daily precipitation predictions, cumulative precipitation predictions are more skillful, with correlation coefficients peaking at 0.7–0.8 after 3–5 days. These results can provide valuable information for water resource managers who are more concerned with the general conditions over a period than with hydrometeorological events occurring on a particular day. This study can guide end users in applying appropriate time scales to fully exploit numerical weather prediction information and satisfy their specific needs.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"361 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135216443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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