Typhoons frequently hit the Pearl River Delta (PRD), threatening the region’s dense population and assets. Typhoon precipitation forecasting in this region is challenging, in part because of the complex hydrometeorological effects over coast and the scarcity of upstream marine meteorological observations. Typhoon Mun was formed in the South China Sea on July 2, 2019, and it brought heavy rainfall to the PRD when its center moved to the Beibu Gulf. During Typhoon Mun, an additional sounding was conducted offshore in the PRD every 12 hours to assess the incremental impact on the skill of precipitation forecasting. A precipitation prediction based on the Weather Research and Forecasting model (WRF) underestimated the 12-hour accumulated precipitation over PRD by 87%, with the Final operational global analysis (FNL) data from the National Centers for Environmental Prediction in the United States of America as initial fields. To address this issue, we implemented a solution by reconstructing the initial field through the assimilation of the additional radiosonde observations using the WRF Three-dimensional Variational (3D-Var) method. The prediction with the new initial fields reduced the rainfall underestimation by 24%. A difference analysis indicates that the planetary boundary layer scheme used in FNL underestimates the low-level temperature and humidity, especially after the rainfall peak. In contrast, assimilation gives a more realistic lower tropospheric structure, significantly enhancing the moisture flux convergence around 925 hPa and divergence around 700 hPa around the PRD. Sensitivity experiments show that assimilating atmospheric thermal (i.e., temperature and humidity) profiles are more helpful than dynamic (wind) profiles in improving the rainfall prediction of the typhoon.
{"title":"Assimilation of additional radiosonde observation helps improve the prediction of typhoon-related rainfall in the Pearl River Delta","authors":"Jianqiao Chen, Bo Han, Qinghua Yang, Hao Luo, Zhipeng Xian, Yunfei Zhang, Xing Li, X. Zhang","doi":"10.1175/jhm-d-23-0024.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0024.1","url":null,"abstract":"\u0000Typhoons frequently hit the Pearl River Delta (PRD), threatening the region’s dense population and assets. Typhoon precipitation forecasting in this region is challenging, in part because of the complex hydrometeorological effects over coast and the scarcity of upstream marine meteorological observations. Typhoon Mun was formed in the South China Sea on July 2, 2019, and it brought heavy rainfall to the PRD when its center moved to the Beibu Gulf. During Typhoon Mun, an additional sounding was conducted offshore in the PRD every 12 hours to assess the incremental impact on the skill of precipitation forecasting. A precipitation prediction based on the Weather Research and Forecasting model (WRF) underestimated the 12-hour accumulated precipitation over PRD by 87%, with the Final operational global analysis (FNL) data from the National Centers for Environmental Prediction in the United States of America as initial fields. To address this issue, we implemented a solution by reconstructing the initial field through the assimilation of the additional radiosonde observations using the WRF Three-dimensional Variational (3D-Var) method. The prediction with the new initial fields reduced the rainfall underestimation by 24%. A difference analysis indicates that the planetary boundary layer scheme used in FNL underestimates the low-level temperature and humidity, especially after the rainfall peak. In contrast, assimilation gives a more realistic lower tropospheric structure, significantly enhancing the moisture flux convergence around 925 hPa and divergence around 700 hPa around the PRD. Sensitivity experiments show that assimilating atmospheric thermal (i.e., temperature and humidity) profiles are more helpful than dynamic (wind) profiles in improving the rainfall prediction of the typhoon.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"26 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80987655","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}
Guo Yu, B. Hatchett, Julianne J. Miller, M. Berli, D. Wright, J. Mejía
In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate a catalog of 52 flash-flood-producing storms over the 1996-2021 period for the arid Las Vegas Wash watershed using rain gage observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American Monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are “intensity driven” in the warm season but “volume driven” in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality.
{"title":"Seasonal Storm Characteristics Govern Urban Flash Floods: Insights from the Arid Las Vegas Wash Watershed","authors":"Guo Yu, B. Hatchett, Julianne J. Miller, M. Berli, D. Wright, J. Mejía","doi":"10.1175/jhm-d-23-0002.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0002.1","url":null,"abstract":"\u0000In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate a catalog of 52 flash-flood-producing storms over the 1996-2021 period for the arid Las Vegas Wash watershed using rain gage observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American Monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are “intensity driven” in the warm season but “volume driven” in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"9 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84623778","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}
High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resources management and emergency responses, particularly for small watersheds such as those in Hawaiʻi, USA. Unfortunately, fine temporal (sub-daily) and spatial (< 1-km) resolution of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth”. There are potential advantages to combining the two, which have not been fully exlored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m by 250 m gridded dataset for the tropical island of Oʻahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, Kona low, and a mix of upper-level trough and Kona low) and different rainfall structures (e.g., stratiform and convective). KED merged rainfall estimates outperformed both the radar only and gauge only datasets by: (1) reducing the error from radar rainfall; and (2) improving the underestimation issues from gauge rainfall, particularly during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures.
高时空分辨率降水数据集对于水文和洪水建模至关重要，有助于水资源管理和应急响应，特别是对于美国夏威夷等小流域。不幸的是，精细的时间(次日)和空间(< 1公里)分辨率的降雨数据集并不总是易于应用。雷达以合理的时间分辨率提供大空间范围内降雨率的间接测量，而雨量计提供“地面实况”。将两者结合起来有潜在的好处，这在热带岛屿上还没有得到充分的探索。在这项研究中，我们应用外部漂移克里格(KED)将每小时的测量和雷达降雨量整合到热带奥瓦胡岛的250 m × 250 m网格数据集中。对18个强风暴事件进行了留一交叉验证，包括5种不同的风暴类型(如热带气旋、冷锋、高空低槽、科纳低压以及高空低槽和科纳低压混合)和不同的降雨结构(如层状和对流)。KED合并降水估计优于仅雷达和仅测量数据集:(1)减少了雷达降水的误差;(2)改善雨量计的低估问题，特别是对流降雨。我们证实，KED方法可以用于合并雷达和测量数据，以产生可靠的降雨量估计，特别是对于热带山区岛屿上的风暴事件。此外，在描述不同风暴类型和降雨结构的空间分布和最大降雨量值方面，KED降水估计始终更准确。
{"title":"Deriving gridded hourly rainfall on Oʻahu by combining gauge and radar rainfall","authors":"Yu-Fen Huang, Y. Tsang, A. Nugent","doi":"10.1175/jhm-d-22-0196.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0196.1","url":null,"abstract":"\u0000High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resources management and emergency responses, particularly for small watersheds such as those in Hawaiʻi, USA. Unfortunately, fine temporal (sub-daily) and spatial (< 1-km) resolution of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth”. There are potential advantages to combining the two, which have not been fully exlored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m by 250 m gridded dataset for the tropical island of Oʻahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, Kona low, and a mix of upper-level trough and Kona low) and different rainfall structures (e.g., stratiform and convective). KED merged rainfall estimates outperformed both the radar only and gauge only datasets by: (1) reducing the error from radar rainfall; and (2) improving the underestimation issues from gauge rainfall, particularly during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"47 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84670042","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}
Ho Junho, Guifu Zhang, Petar Bukovcic, D. Parsons, Feng Xu, Jidong Gao, Jacob T. Carlin, J. Snyder
Rain drop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–2017. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event-average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root mean squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain rate estimate bias of the DNN was significantly reduced (3.3% in DNN versus 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations.
{"title":"Improving Polarimetric Radar-based Drop Size Distribution Retrieval and Rain Estimation using Deep Neural Network","authors":"Ho Junho, Guifu Zhang, Petar Bukovcic, D. Parsons, Feng Xu, Jidong Gao, Jacob T. Carlin, J. Snyder","doi":"10.1175/jhm-d-22-0166.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0166.1","url":null,"abstract":"Rain drop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–2017. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event-average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root mean squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain rate estimate bias of the DNN was significantly reduced (3.3% in DNN versus 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"53 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74993025","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}
Moumouni Djibo, C. Chwala, Maximilian Graf, Julius Polz, H. Kunstmann, F. Zougmore
We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-minute temporal and 0.55km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a five-minute and aggregated daily scale. We observed high spatio-temporal variability on the five-minute scale which cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement with a Pearson correlation coefficient of over 0.95 and an underestimation of the CML rainfall maps of around ten percent. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and around half of all CMLs were removed. We further compared ERA5 and GPM-IMERG data to the rain gauge and found that they show much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data scarce but densely populated African cities.
{"title":"High-resolution rainfall maps from commercial microwave links for a data-scarce region in West Africa","authors":"Moumouni Djibo, C. Chwala, Maximilian Graf, Julius Polz, H. Kunstmann, F. Zougmore","doi":"10.1175/jhm-d-23-0015.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0015.1","url":null,"abstract":"\u0000We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-minute temporal and 0.55km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a five-minute and aggregated daily scale. We observed high spatio-temporal variability on the five-minute scale which cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement with a Pearson correlation coefficient of over 0.95 and an underestimation of the CML rainfall maps of around ten percent. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and around half of all CMLs were removed. We further compared ERA5 and GPM-IMERG data to the rain gauge and found that they show much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data scarce but densely populated African cities.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"81 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76280042","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}
E. Zhu, C. Shi, Shuai Sun, Binghao Jia, Yaqiang Wang, X. Yuan
Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, the small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021-2022. Compared to the open loop experiment (without SCF assimilation), the root mean square error (RMSE) of SCF is reduced by 6% through the original EnSRF, and is even lower (by 14%) in the EnSRFDI (i.e., combined DI and EnSRF) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling-Gupta efficiency (KGE) increasing at 60% and 56%-70% stations respectively, particularly under conditions with near-freezing temperature, where reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products.
{"title":"Hybrid Assimilation of Snow Cover Improves Land Surface Simulations over Northern China","authors":"E. Zhu, C. Shi, Shuai Sun, Binghao Jia, Yaqiang Wang, X. Yuan","doi":"10.1175/jhm-d-23-0014.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0014.1","url":null,"abstract":"\u0000Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, the small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021-2022. Compared to the open loop experiment (without SCF assimilation), the root mean square error (RMSE) of SCF is reduced by 6% through the original EnSRF, and is even lower (by 14%) in the EnSRFDI (i.e., combined DI and EnSRF) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling-Gupta efficiency (KGE) increasing at 60% and 56%-70% stations respectively, particularly under conditions with near-freezing temperature, where reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"65 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84788836","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}
Thorough evaluations of satellite precipitation products are necessary for accurately detecting meteorological drought. A comprehensive assessment of 15 state-of-the-art precipitation products (i.e., IMERG_cal, IMERG_uncal, GSMaP-G, CPC-Global, TRMM3B42, CMORPH-CRT, PERSIANN-CDR, PERSIANN, PERSIANN-CCS, SM2RAIN, CHIRPS, ERA5, ERA-interim, MERRA2, and GLDAS) is herein conducted for the period 2010 to 2019 giving special attention to their performance in detecting meteorological drought over mainland China at 0.25° spatial resolution. The cited precipitation products are compared against China’s gridded gauge-based Daily Precipitation Analysis (CGDPA) product, derived from 2400 meteorological stations, and their quality is assessed at daily, seasonal, and annual precipitation timescales. Meteorological droughts in the datasets are determined by calculating the Standardized Precipitation Evapotranspiration Index (SPEI). The performance of the precipitation products for drought detection with respect to the SPEI is assessed at three timescales (1-, 3-, and 12-month). The results show that the GSMaP-G outperforms other satellite-based datasets in drought detection and precipitation estimation. The MERRA2 and the ERA5 are on average closer to the CGDPA reference data than other reanalysis products for precipitation estimation and drought detection. These products capture well the spatial and temporal pattern of the SPEI in southern and eastern China having a probability of detection (PODs) above 0.6 and a correlation coefficient (CC) above 0.65. CPC-Global, IMERG satellite, and the ERA5 reanalysis product are ideal candidates for application in western China, especially in the Qinghai-Tibetan plateau and the Xinjiang Province. Generally, the accuracy of precipitation products for drought detection is improved with longer timescales of the SPEI (i.e., SPEI-12). This study contributes to drought-hazard detection and hydrometeorological applications of satellite precipitation products.
{"title":"Comprehensive evaluation of global precipitation products and their accuracy in drought detection in mainland China","authors":"Huihui Zhang, H. Loáiciga, Qingyun Du, T. Sauter","doi":"10.1175/jhm-d-22-0233.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0233.1","url":null,"abstract":"\u0000Thorough evaluations of satellite precipitation products are necessary for accurately detecting meteorological drought. A comprehensive assessment of 15 state-of-the-art precipitation products (i.e., IMERG_cal, IMERG_uncal, GSMaP-G, CPC-Global, TRMM3B42, CMORPH-CRT, PERSIANN-CDR, PERSIANN, PERSIANN-CCS, SM2RAIN, CHIRPS, ERA5, ERA-interim, MERRA2, and GLDAS) is herein conducted for the period 2010 to 2019 giving special attention to their performance in detecting meteorological drought over mainland China at 0.25° spatial resolution. The cited precipitation products are compared against China’s gridded gauge-based Daily Precipitation Analysis (CGDPA) product, derived from 2400 meteorological stations, and their quality is assessed at daily, seasonal, and annual precipitation timescales. Meteorological droughts in the datasets are determined by calculating the Standardized Precipitation Evapotranspiration Index (SPEI). The performance of the precipitation products for drought detection with respect to the SPEI is assessed at three timescales (1-, 3-, and 12-month). The results show that the GSMaP-G outperforms other satellite-based datasets in drought detection and precipitation estimation. The MERRA2 and the ERA5 are on average closer to the CGDPA reference data than other reanalysis products for precipitation estimation and drought detection. These products capture well the spatial and temporal pattern of the SPEI in southern and eastern China having a probability of detection (PODs) above 0.6 and a correlation coefficient (CC) above 0.65. CPC-Global, IMERG satellite, and the ERA5 reanalysis product are ideal candidates for application in western China, especially in the Qinghai-Tibetan plateau and the Xinjiang Province. Generally, the accuracy of precipitation products for drought detection is improved with longer timescales of the SPEI (i.e., SPEI-12). This study contributes to drought-hazard detection and hydrometeorological applications of satellite precipitation products.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"97 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74878671","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}
Ebrahim Ghaderpour, M. Zaghloul, H. Dastour, Anil K. Gupta, G. Achari, Q. Hassan
River flow monitoring is a critical task for land management, agriculture, fishery, industry, and others. Herein, a robust least-squares triple cross-wavelet analysis is proposed to investigate possible relationships between river flow, temperature, and precipitation in the time-frequency domain. The Athabasca River Basin (ARB) in Canada is selected as a case study to investigate such relationships. The historical climate and river flow datasets since 1950 for three homogeneous subregions of ARB were analyzed using a traditional multivariate regression model and the proposed wavelet analysis. The highest Pearson correlation (0.87) was estimated between all the monthly averaged river flow, temperature, and accumulated precipitation for the subregion between Hinton and Athabasca. The highest and lowest correlations between climate and river flow were found to be during the open warm season and cold season, respectively. Particularly, the highest correlations between temperature, precipitation, and river flow were in May (0.78) for Hinton, July (0.54) for Athabasca, and September (0.44) for Fort McMurray. The new wavelet analysis revealed significant coherency between annual cycles of climate and river flow for the three subregions, with the highest of 33.7% for Fort McMurray and the lowest of 4.7% for Hinton with more coherency since 1991. The phase delay analysis showed that annual and semiannual cycles of precipitation generally led the ones in river flow by a few weeks mainly for upper and middle ARB since 1991. The climate and river flow anomalies were also demonstrated using the baseline period 1961-1990, showing a significant increase in temperature and decrease in precipitation since 1991 for all the three subregions. Unlike the multivariate regression, the proposed wavelet method can analyze any hydrometeorological time series in the time-frequency domain without any need for resampling, interpolation, or gap filling.
{"title":"Least-Squares Triple Cross-Wavelet and Multivariate Regression Analyses of Climate and River Flow in Athabasca River Basin","authors":"Ebrahim Ghaderpour, M. Zaghloul, H. Dastour, Anil K. Gupta, G. Achari, Q. Hassan","doi":"10.1175/jhm-d-23-0013.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0013.1","url":null,"abstract":"\u0000River flow monitoring is a critical task for land management, agriculture, fishery, industry, and others. Herein, a robust least-squares triple cross-wavelet analysis is proposed to investigate possible relationships between river flow, temperature, and precipitation in the time-frequency domain. The Athabasca River Basin (ARB) in Canada is selected as a case study to investigate such relationships. The historical climate and river flow datasets since 1950 for three homogeneous subregions of ARB were analyzed using a traditional multivariate regression model and the proposed wavelet analysis. The highest Pearson correlation (0.87) was estimated between all the monthly averaged river flow, temperature, and accumulated precipitation for the subregion between Hinton and Athabasca. The highest and lowest correlations between climate and river flow were found to be during the open warm season and cold season, respectively. Particularly, the highest correlations between temperature, precipitation, and river flow were in May (0.78) for Hinton, July (0.54) for Athabasca, and September (0.44) for Fort McMurray. The new wavelet analysis revealed significant coherency between annual cycles of climate and river flow for the three subregions, with the highest of 33.7% for Fort McMurray and the lowest of 4.7% for Hinton with more coherency since 1991. The phase delay analysis showed that annual and semiannual cycles of precipitation generally led the ones in river flow by a few weeks mainly for upper and middle ARB since 1991. The climate and river flow anomalies were also demonstrated using the baseline period 1961-1990, showing a significant increase in temperature and decrease in precipitation since 1991 for all the three subregions. Unlike the multivariate regression, the proposed wavelet method can analyze any hydrometeorological time series in the time-frequency domain without any need for resampling, interpolation, or gap filling.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"34 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74480291","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}
Severe rainstorm is one of the most devastating disasters in southeast China (SEC). A deep and comprehensive understanding of the spatial correlations of severe rainstorms is important for preventing rainstorm-induced hazards. In this study, tropical cyclone- and non-tropical cyclone-induced severe rainstorms (TCSRs and NTCSRs) over SEC during 2000 - 2019 are discussed. Co-occurrence probability and range values calculated using semivariogram method are used to measure the spatial correlation of severe rainstorms. The extent to which potential factors (El Niño/La Niña, Indian Ocean Dipole (IOD), latitudes, longitudes, temperature, elevation, and radius of maximum wind) affect the spatial structure of severe rainstorms are discussed. The spatial correlation distances for TCSRs (300 - 700 km) in Typhoon season (July, August, and September) are longer than most of those for NTCSRs (150 - 300 km) in Meiyu season (June and July). The range values of TCSRs at each percentile (except for the minimum range values) tend to be omnidirectional. While NTCSRs tend to have the major direction of NE-SW. El Niño tends to increase the average spatial correlation distance of TCSRs in NE-SW and NTCSRs in N-NE. La Niña tends to decrease the spatial correlation distance of TCSRs in NE-SW. The occurrence of positive IOD and negative IOD (-IOD) events may increase the spatial correlation distance of TCSRs in NW-SE, and -IOD events may decrease the distance in NE-SW. IOD events especially -IOD may change the spatial correlation distance of NTCSRs in E-NE. Latitudes, longitudes, temperature, elevation, and radius of maximum wind significantly affect the spatial correlation distance of TCSRs in various directions.
{"title":"Spatial correlations of regional tropical cyclone- and non-tropical cyclone-induced severe rainstorms during 2000 - 2019","authors":"Yuanyuan Zhou, Haoxuan Du, Liang Gao","doi":"10.1175/jhm-d-22-0145.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0145.1","url":null,"abstract":"\u0000Severe rainstorm is one of the most devastating disasters in southeast China (SEC). A deep and comprehensive understanding of the spatial correlations of severe rainstorms is important for preventing rainstorm-induced hazards. In this study, tropical cyclone- and non-tropical cyclone-induced severe rainstorms (TCSRs and NTCSRs) over SEC during 2000 - 2019 are discussed. Co-occurrence probability and range values calculated using semivariogram method are used to measure the spatial correlation of severe rainstorms. The extent to which potential factors (El Niño/La Niña, Indian Ocean Dipole (IOD), latitudes, longitudes, temperature, elevation, and radius of maximum wind) affect the spatial structure of severe rainstorms are discussed. The spatial correlation distances for TCSRs (300 - 700 km) in Typhoon season (July, August, and September) are longer than most of those for NTCSRs (150 - 300 km) in Meiyu season (June and July). The range values of TCSRs at each percentile (except for the minimum range values) tend to be omnidirectional. While NTCSRs tend to have the major direction of NE-SW. El Niño tends to increase the average spatial correlation distance of TCSRs in NE-SW and NTCSRs in N-NE. La Niña tends to decrease the spatial correlation distance of TCSRs in NE-SW. The occurrence of positive IOD and negative IOD (-IOD) events may increase the spatial correlation distance of TCSRs in NW-SE, and -IOD events may decrease the distance in NE-SW. IOD events especially -IOD may change the spatial correlation distance of NTCSRs in E-NE. Latitudes, longitudes, temperature, elevation, and radius of maximum wind significantly affect the spatial correlation distance of TCSRs in various directions.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"23 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76811809","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}
Eurasian spring snowmelt plays an important role in the subsequent climate and hydrological cycle, however, the understanding of snowmelt itself and its causes remains insufficient. This study explored the basic characteristics of spring snowmelt in the eastern Europe–western Siberia (EEWS) region by classifying snowmelt anomalies into two categories based on the different factors that dominate spring snowmelt, and then investigated the associated atmospheric circulation anomalies and local physical processes. The first category of anomalous snowmelt (category 1) is controlled by both the initial snow mass and the later snowmelt process, while the second category of anomalous snowmelt (category 2) is mainly linked to the later snowmelt process. Specifically, category 1 is characterized by an anomalous trough in EEWS in winter, where water vapor transported and converged, accompanied by anomalous upward motion, which promotes snowfall and snow accumulation, providing initial conditions conducive to snowmelt. In April, this region is controlled by an anomalous ridge, with significant warm advection anomalies and subsidence promoting surface warming, thereby accelerating snow melting. In contrast, the winter circulation anomalies are insignificant in category 2, while the anomalous ridge in April is stronger than in category 1, accompanied by more intense snowmelt processes. In addition, from the surface energy balance perspective, atmospheric downward sensible heat transport is an important factor influencing the anomalous snowmelt in category 1, while shortwave radiation plays a secondary role. Conversely, the snowmelt in category 2 is dominated by shortwave radiation forcing, but the sensible heat effect is slightly weaker. Eurasian spring snowmelt significantly impacts the subsequent climate and hydrological cycle, but the understanding of snowmelt itself and its causes is still inadequate. The purpose of this study is to explore the monthly evolution of atmospheric circulation associated with anomalous snowmelt and its local physical processes associated by categorizing them based on snowmelt characteristics. Category 1 is jointly affected by winter snow accumulation and later warming, while category 2 is dominated by strong snowmelt process in late spring. These two categories are accompanied by different winter and spring circulation configurations. Our results provide a basis for further investigation of snowmelt precursor signals.
{"title":"Atmospheric Circulation Anomalies and Key Physical Processes behind Two Categories of Anomalous Eurasian Spring Snowmelt","authors":"Yue Sun, Haishan Chen","doi":"10.1175/jhm-d-23-0010.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0010.1","url":null,"abstract":"\u0000Eurasian spring snowmelt plays an important role in the subsequent climate and hydrological cycle, however, the understanding of snowmelt itself and its causes remains insufficient. This study explored the basic characteristics of spring snowmelt in the eastern Europe–western Siberia (EEWS) region by classifying snowmelt anomalies into two categories based on the different factors that dominate spring snowmelt, and then investigated the associated atmospheric circulation anomalies and local physical processes. The first category of anomalous snowmelt (category 1) is controlled by both the initial snow mass and the later snowmelt process, while the second category of anomalous snowmelt (category 2) is mainly linked to the later snowmelt process. Specifically, category 1 is characterized by an anomalous trough in EEWS in winter, where water vapor transported and converged, accompanied by anomalous upward motion, which promotes snowfall and snow accumulation, providing initial conditions conducive to snowmelt. In April, this region is controlled by an anomalous ridge, with significant warm advection anomalies and subsidence promoting surface warming, thereby accelerating snow melting. In contrast, the winter circulation anomalies are insignificant in category 2, while the anomalous ridge in April is stronger than in category 1, accompanied by more intense snowmelt processes. In addition, from the surface energy balance perspective, atmospheric downward sensible heat transport is an important factor influencing the anomalous snowmelt in category 1, while shortwave radiation plays a secondary role. Conversely, the snowmelt in category 2 is dominated by shortwave radiation forcing, but the sensible heat effect is slightly weaker.\u0000\u0000\u0000Eurasian spring snowmelt significantly impacts the subsequent climate and hydrological cycle, but the understanding of snowmelt itself and its causes is still inadequate. The purpose of this study is to explore the monthly evolution of atmospheric circulation associated with anomalous snowmelt and its local physical processes associated by categorizing them based on snowmelt characteristics. Category 1 is jointly affected by winter snow accumulation and later warming, while category 2 is dominated by strong snowmelt process in late spring. These two categories are accompanied by different winter and spring circulation configurations. Our results provide a basis for further investigation of snowmelt precursor signals.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"200 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83763734","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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