Vesta Afzali Gorooh, Veljko Petković, Malarvizhi Arulraj, Phu Nguyen, Kuo-lin Hsu, Soroosh Sorooshian, Ralph R. Ferraro
Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.
{"title":"Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval","authors":"Vesta Afzali Gorooh, Veljko Petković, Malarvizhi Arulraj, Phu Nguyen, Kuo-lin Hsu, Soroosh Sorooshian, Ralph R. Ferraro","doi":"10.1175/jhm-d-23-0006.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0006.1","url":null,"abstract":"Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"35 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111210","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}
Yalan Song, Wen-Ping Tsai, Jonah Gluck, Alan Rhoades, Colin Zarzycki, Rachel McCrary, Kathryn Lawson, Chaopeng Shen
Abstract Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western US to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow-and deep-snow sites. The median Nash-Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values ( d max ) were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors which would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern US, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for non-ephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.
{"title":"LSTM-based data integration to improve snow water equivalent prediction and diagnose error sources","authors":"Yalan Song, Wen-Ping Tsai, Jonah Gluck, Alan Rhoades, Colin Zarzycki, Rachel McCrary, Kathryn Lawson, Chaopeng Shen","doi":"10.1175/jhm-d-22-0220.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0220.1","url":null,"abstract":"Abstract Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western US to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow-and deep-snow sites. The median Nash-Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values ( d max ) were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors which would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern US, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for non-ephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"31 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068385","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}
W.J. Rudisill, A.N. Flores, H.P. Marshall, E. Siirila-Woodburn, D.R. Feldman, A.M. Rhoades, Z. Xu, A. Morales
Abstract Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are conducted for two cold-seasons (2018 and 2019) centered on the Colorado Rockies’ ∼750 km 2 East River Watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land-surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that microphysical parameterization choice alone has a modest impact on total precipitation on the order of ± 3% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15 ± 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8-17 cm and percent-biases of 3-15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization, but conclude that snow lidar is a valuable non-traditional indicator of model performance.
{"title":"Cold-Season Precipitation Sensitivity to Microphysical Parameterizations: Hydrologic Evaluations Leveraging Snow Lidar Datasets","authors":"W.J. Rudisill, A.N. Flores, H.P. Marshall, E. Siirila-Woodburn, D.R. Feldman, A.M. Rhoades, Z. Xu, A. Morales","doi":"10.1175/jhm-d-22-0217.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0217.1","url":null,"abstract":"Abstract Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are conducted for two cold-seasons (2018 and 2019) centered on the Colorado Rockies’ ∼750 km 2 East River Watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land-surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that microphysical parameterization choice alone has a modest impact on total precipitation on the order of ± 3% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15 ± 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8-17 cm and percent-biases of 3-15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization, but conclude that snow lidar is a valuable non-traditional indicator of model performance.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067932","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 This study explores the population exposure to an increasing number of hydroclimatic extreme events owing to the warming climate. It is well-agreed that the extreme events are increasing in terms of frequency as well as intensity due to climate change and that the exposure to compound extreme events (concurrent occurrence of two or more extreme phenomena) affects population, ecosystems, and a variety of socioeconomic aspects more adversely. Specifically, the compound precipitation-temperature extremes (hot-dry and hot-wet) are considered, and the entire Indian mainland is regarded as the study region that spans over a wide variety of climatic regimes and wide variation of population density. The developed copula-based statistical method evaluates the change in population exposure to the compound extremes across the past (1981-2020) and future (near future: 2021-2060 and far future: 2061-2100) due to climate change. The results indicate an increase of more than 10 million person-year exposure from the compound extremes across many regions of the country, considering both near and far future periods. Densely populated regions have experienced more significant changes in hot-wet extremes as compared to the hot-dry extremes in the past, and the same is projected to continue in the future. The increase is as much as six-fold in many parts of the country, including Indo-Gangetic plains and southern-most coastal regions, identified as the future hotspots with the maximum increase in exposure under all the projected warming and population scenarios. The study helps to identify the regions that may need greater attention based on the risks of population exposure to compound extremes in a warmer future.
{"title":"Population Exposure to Compound Precipitation-Temperature Extremes in the Past and Future Climate across India","authors":"Subhasmita Dash, Rajib Maity, Harald Kunstmann","doi":"10.1175/jhm-d-22-0238.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0238.1","url":null,"abstract":"Abstract This study explores the population exposure to an increasing number of hydroclimatic extreme events owing to the warming climate. It is well-agreed that the extreme events are increasing in terms of frequency as well as intensity due to climate change and that the exposure to compound extreme events (concurrent occurrence of two or more extreme phenomena) affects population, ecosystems, and a variety of socioeconomic aspects more adversely. Specifically, the compound precipitation-temperature extremes (hot-dry and hot-wet) are considered, and the entire Indian mainland is regarded as the study region that spans over a wide variety of climatic regimes and wide variation of population density. The developed copula-based statistical method evaluates the change in population exposure to the compound extremes across the past (1981-2020) and future (near future: 2021-2060 and far future: 2061-2100) due to climate change. The results indicate an increase of more than 10 million person-year exposure from the compound extremes across many regions of the country, considering both near and far future periods. Densely populated regions have experienced more significant changes in hot-wet extremes as compared to the hot-dry extremes in the past, and the same is projected to continue in the future. The increase is as much as six-fold in many parts of the country, including Indo-Gangetic plains and southern-most coastal regions, identified as the future hotspots with the maximum increase in exposure under all the projected warming and population scenarios. The study helps to identify the regions that may need greater attention based on the risks of population exposure to compound extremes in a warmer future.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"138 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136316476","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}
Pei-Ning Feng, Stéphane Bélair, Dikraa Khedhaouiria, Franck Lespinas, Eva Mekis, Julie M. Thériault
Abstract The Canadian Precipitation Analysis System (CaPA) is an operational system that uses a combination of weather gauge and ground-based radar measurements together with short-term forecasts from a numerical weather model to provide near-real-time estimates of 6 and 24-hour precipitation amounts. During the winter season, many gauge measurements are rejected by the CaPA quality control process due to the wind-induced undercatch for solid precipitation. The goal of this study is to improve the precipitation estimates over central Canada during the winter seasons from 2019 to 2022. Two approaches were tested. First, the quality control procedure in CaPA has been relaxed to increase the number of surface observations assimilated. Second, the automatic solid precipitation measurements were adjusted using a universal transfer function to compensate for the undercatch problem. Although increasing the wind speed threshold resulted in lower amounts and worse biases in frequency, the overall precipitation estimates is improved as the equitable threat score is improved due to a substantial decrease in the false alarm ratio, which compensates the degradation of the probability of detection. The increase of solid precipitation amounts using a transfer function improves the biases in both frequency and amounts, and the probability of detection for all precipitation thresholds. However, the false alarm ratio deteriorates for large thresholds. The statistics varies from year to year, but an overall improvement is demonstrated by increasing the number of stations and adjusting the solid precipitation amounts for wind speed undercatch.
{"title":"Impact of adjusted and non-adjusted surface observations on the cold season performance of the Canadian Precipitation Analysis (CaPA) System","authors":"Pei-Ning Feng, Stéphane Bélair, Dikraa Khedhaouiria, Franck Lespinas, Eva Mekis, Julie M. Thériault","doi":"10.1175/jhm-d-23-0070.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0070.1","url":null,"abstract":"Abstract The Canadian Precipitation Analysis System (CaPA) is an operational system that uses a combination of weather gauge and ground-based radar measurements together with short-term forecasts from a numerical weather model to provide near-real-time estimates of 6 and 24-hour precipitation amounts. During the winter season, many gauge measurements are rejected by the CaPA quality control process due to the wind-induced undercatch for solid precipitation. The goal of this study is to improve the precipitation estimates over central Canada during the winter seasons from 2019 to 2022. Two approaches were tested. First, the quality control procedure in CaPA has been relaxed to increase the number of surface observations assimilated. Second, the automatic solid precipitation measurements were adjusted using a universal transfer function to compensate for the undercatch problem. Although increasing the wind speed threshold resulted in lower amounts and worse biases in frequency, the overall precipitation estimates is improved as the equitable threat score is improved due to a substantial decrease in the false alarm ratio, which compensates the degradation of the probability of detection. The increase of solid precipitation amounts using a transfer function improves the biases in both frequency and amounts, and the probability of detection for all precipitation thresholds. However, the false alarm ratio deteriorates for large thresholds. The statistics varies from year to year, but an overall improvement is demonstrated by increasing the number of stations and adjusting the solid precipitation amounts for wind speed undercatch.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"15 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263738","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 Snow cover provides distinct seasonal controls on the exchange of energy between the Earth’s surface and atmosphere, hydrologic cycling, and holds considerable importance to communities and ecosystems worldwide. In this work, we tackle a comprehensive review of existing snow classification approaches and the development of new globally applicable snow cover-based rules for delineating snow seasonality classes. Snow classification rules are defined using machine learning approaches, which are then applied to the 22-year record of snow cover (2000-2022) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a 0.01° global grid. For the MODIS period of record, we find the global land surface can be effectively partitioned into five snow seasonality classes: no snow, ephemeral, transitional, seasonal, and perennial snow regimes which on average cover extents of approximately 76 (52% of global land areas), 19 (13%), 16 (11%), 18 (13%), and 16 million km 2 (11%), respectively. Using the multi-decadal dataset, we explore changes within snow regimes and find significant increases in the areal extent of no snow (approximately +70,000 km 2 /year) as well as apparent losses in perennial (‒3,600 km 2 /year) and seasonal snow regime coverage (‒38,000 km 2 /year). The resulting classification maps have strong agreement with in-situ snow depth observations and present similar patterns to existing snow and climate classifications with notable discrepancies in cold arid regions. The framework's ability to accurately capture variations in snow persistence, snow accumulation, and melt cycling is shown, providing a reference to the current state of global snow seasonality.
积雪对地表和大气之间的能量交换、水文循环提供了明显的季节性控制，对全球的群落和生态系统具有相当重要的意义。在这项工作中，我们对现有的积雪分类方法进行了全面的回顾，并开发了新的全球适用的基于积雪覆盖的雪季节性分类规则。使用机器学习方法定义积雪分类规则，然后将其应用于中分辨率成像光谱仪(MODIS)在0.01°全球网格上的22年积雪记录(2000-2022)。对于MODIS记录期，我们发现全球陆地表面可以有效地划分为5个雪季节类型:无雪、短暂、过渡、季节性和多年生雪状态，平均覆盖范围分别约为76(占全球陆地面积的52%)、19(13%)、16(11%)、18(13%)和1600万km 2(11%)。利用多年代际数据集，我们探索了雪况的变化，发现无雪面积(约+70,000 km2 /年)显著增加，以及多年生(-3,600 km2 /年)和季节性雪况覆盖的表观损失(-38,000 km2 /年)。所得分类图与现场雪深观测结果具有较强的一致性，与现有的雪和气候分类模式相似，但在寒冷干旱地区差异显著。该框架能够准确地捕捉积雪持续、积雪积累和融化循环的变化，为全球积雪季节性的当前状态提供参考。
{"title":"Global Snow Seasonality Regimes from Satellite Records of Snow Cover","authors":"Jeremy Johnston, Jennifer M. Jacobs, Eunsang Cho","doi":"10.1175/jhm-d-23-0047.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0047.1","url":null,"abstract":"Abstract Snow cover provides distinct seasonal controls on the exchange of energy between the Earth’s surface and atmosphere, hydrologic cycling, and holds considerable importance to communities and ecosystems worldwide. In this work, we tackle a comprehensive review of existing snow classification approaches and the development of new globally applicable snow cover-based rules for delineating snow seasonality classes. Snow classification rules are defined using machine learning approaches, which are then applied to the 22-year record of snow cover (2000-2022) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a 0.01° global grid. For the MODIS period of record, we find the global land surface can be effectively partitioned into five snow seasonality classes: no snow, ephemeral, transitional, seasonal, and perennial snow regimes which on average cover extents of approximately 76 (52% of global land areas), 19 (13%), 16 (11%), 18 (13%), and 16 million km 2 (11%), respectively. Using the multi-decadal dataset, we explore changes within snow regimes and find significant increases in the areal extent of no snow (approximately +70,000 km 2 /year) as well as apparent losses in perennial (‒3,600 km 2 /year) and seasonal snow regime coverage (‒38,000 km 2 /year). The resulting classification maps have strong agreement with in-situ snow depth observations and present similar patterns to existing snow and climate classifications with notable discrepancies in cold arid regions. The framework's ability to accurately capture variations in snow persistence, snow accumulation, and melt cycling is shown, providing a reference to the current state of global snow seasonality.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"15 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412956","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 Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water-state/water-flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state/flux coupling strength bias - involving both evapotranspiration and runoff - are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias – even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The re-scaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water-state/water-flux coupling strength biases during the operation of an LDAS.
{"title":"Systematic modelling errors undermine the application of land data assimilation systems for hydrological and weather forecasting","authors":"Wade T. Crow, Hyunglok Kim, Sujay Kumar","doi":"10.1175/jhm-d-23-0069.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0069.1","url":null,"abstract":"Abstract Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water-state/water-flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state/flux coupling strength bias - involving both evapotranspiration and runoff - are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias – even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The re-scaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water-state/water-flux coupling strength biases during the operation of an LDAS.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617023","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 Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydro-meteorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement mission (GPM) Dual-Frequency Precipitation Radar (DPR) can estimate precipitation and cloud properties, and has a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss-Waldvogel disdrometers from 2014 to 2021. The precipitation and drop size distribution parameters like rainfall rate ( R , mm h −1 ), radar reflectivity factor (dBZ), mass-weighted mean drop diameter ( D m , mm), and normalized intercept parameter ( N w , m −3 mm −1 ) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5 km average, 10 km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual- or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter.
全球降水在水文循环中发挥着重要作用，在水文气象研究中具有重要意义。先进的遥感仪器，如美国宇航局全球降水测量任务(GPM)双频降水雷达(DPR)可以估计降水和云的性质，并具有独特的能力，可以及时估计全球快照的雨滴大小信息。本研究以2014年至2021年台湾北部7台乔斯-瓦尔德沃格仪的长期测量数据，验证了GPM DPR的二级数据产品。对比了GPM DPR的降雨率(R, mm h−1)、雷达反射率因子(dBZ)、质量加权平均雨滴直径(D m, mm)和归一化截距参数(N w, m−3 mm−1)等降水和雨滴大小分布参数。采用4种不同的比较方法(点匹配、平均5公里、平均10公里和最优方法)进行验证;其中，最优策略能使GPM DPR与液位计之间达到合理的一致性。GPM DPR在层状降水中对降雨参数的估计优于对流降水。无论算法类型(双频或单频算法)，敏感性分析显示质量加权平均直径的一致性较好，而归一化截距参数的一致性较差。
{"title":"Evaluation of GPM DPR rain parameters with north Taiwan disdrometers","authors":"Seela Balaji Kumar, Jayalakshmi Janapati, Pay-Liam Lin, Chen-Hau Lan, Mu-Qun Huang","doi":"10.1175/jhm-d-23-0027.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0027.1","url":null,"abstract":"Abstract Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydro-meteorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement mission (GPM) Dual-Frequency Precipitation Radar (DPR) can estimate precipitation and cloud properties, and has a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss-Waldvogel disdrometers from 2014 to 2021. The precipitation and drop size distribution parameters like rainfall rate ( R , mm h −1 ), radar reflectivity factor (dBZ), mass-weighted mean drop diameter ( D m , mm), and normalized intercept parameter ( N w , m −3 mm −1 ) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5 km average, 10 km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual- or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098479","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}
Chak-Hau Michael Tso, Eleanor Blyth, Maliko Tanguy, Peter E. Levy, Emma L. Robinson, Victoria Bell, Yuanyuan Zha, Matthew Fry
Abstract The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in-situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple UK soil moisture products, including satellite-merged (i.e. TCM), in-situ (i.e. COSMOS-UK), hydrological model (i.e. G2G), statistical model (i.e. SMUK) and land surface model (LSM) (i.e. CHESS) data. The drydown decay time scale (τ) for all gridded products are computed at an unprecedented resolution of 1-2 km, a scale relevant to weather and climate models. While their range of τ differ (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyse the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales.
{"title":"Multi-product characterization of surface soil moisture drydowns in the UK","authors":"Chak-Hau Michael Tso, Eleanor Blyth, Maliko Tanguy, Peter E. Levy, Emma L. Robinson, Victoria Bell, Yuanyuan Zha, Matthew Fry","doi":"10.1175/jhm-d-23-0018.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0018.1","url":null,"abstract":"Abstract The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in-situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple UK soil moisture products, including satellite-merged (i.e. TCM), in-situ (i.e. COSMOS-UK), hydrological model (i.e. G2G), statistical model (i.e. SMUK) and land surface model (LSM) (i.e. CHESS) data. The drydown decay time scale (τ) for all gridded products are computed at an unprecedented resolution of 1-2 km, a scale relevant to weather and climate models. While their range of τ differ (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyse the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141803","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}
Xing Wang, Shuaiyi Shi, Litao Zhu, Yunfeng Nie, Guojun Lai
Due to its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from “citizen science” and “crowd-sourcing,” allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors.
{"title":"Traditional and Novel Methods of Rainfall Observation and Measurement: A Review","authors":"Xing Wang, Shuaiyi Shi, Litao Zhu, Yunfeng Nie, Guojun Lai","doi":"10.1175/jhm-d-22-0122.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0122.1","url":null,"abstract":"Due to its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from “citizen science” and “crowd-sourcing,” allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"2 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139323771","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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