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Abstract
Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds.
Significance Statement
Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.
Abstract
Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds.
Significance Statement
Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.
Abstract
A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.
Abstract
A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.
Abstract
Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.
Significance Statement
Flash droughts are a subset of droughts with rapid onset and intensification leading to devastating losses to crops. Rapid soil moisture decline is one way to detect flash droughts. Because there is a lack of widespread observational data, we often rely on model outputs of soil moisture. Here, we explore how the representation of vegetation within land surface models influences the U.S. flash drought characteristics covering 1981–2017. We show that the misrepresentation of vegetation status propagates soil moisture biases into flash drought monitoring, impacting our understanding of the onset, magnitude, duration, and trends in flash droughts. Our results suggest that the assimilation of near-real-time vegetation into land surface models could improve the detection, monitoring, and prediction of flash droughts.
Abstract
Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.
Significance Statement
Flash droughts are a subset of droughts with rapid onset and intensification leading to devastating losses to crops. Rapid soil moisture decline is one way to detect flash droughts. Because there is a lack of widespread observational data, we often rely on model outputs of soil moisture. Here, we explore how the representation of vegetation within land surface models influences the U.S. flash drought characteristics covering 1981–2017. We show that the misrepresentation of vegetation status propagates soil moisture biases into flash drought monitoring, impacting our understanding of the onset, magnitude, duration, and trends in flash droughts. Our results suggest that the assimilation of near-real-time vegetation into land surface models could improve the detection, monitoring, and prediction of flash droughts.
Abstract
Pacific Islands present unique challenges for water resource management due to their environmental vulnerability, dynamic climates, and heavy reliance on groundwater. Quantifying connections between meteoric, ground, and surface waters is critical for effective water resource management. Analyses of the stable isotopes of oxygen and hydrogen in the hydrosphere can help illuminate such connections. This study investigates the stable isotope composition of rainfall on O‘ahu in the Hawaiian Islands, with a particular focus on how altitude impacts stable isotope composition. Rainfall was sampled at 20 locations from March 2018 to August 2021. The new precipitation stable isotope data were integrated with previously published data to create the most spatially and topographically diverse precipitation collector network on O‘ahu to date. Results show that δ 18O and δ 2H values in precipitation displayed distinct isotopic signatures influenced by geographical location, season, and precipitation source. Altitude and isotopic compositions were strongly correlated along certain elevation transects, but these relationships could not be extrapolated to larger regions due to microclimate influences. Altitude and deuterium excess were strongly correlated across the study region, suggesting that deuterium excess may be a reliable proxy for precipitation elevation in local water tracer studies. Analysis of spring, rainfall, and fog stable isotope composition from Mount Ka‘ala suggests that fog may contribute up to 45% of total groundwater recharge at the summit. These findings highlight the strong influence of microclimates on the stable isotope composition of rainfall, underscore the need for further investigation into fog’s role in the water budget, and demonstrate the importance of stable isotope analysis for comprehending hydrologic dynamics in environmentally sensitive regions.
Abstract
Pacific Islands present unique challenges for water resource management due to their environmental vulnerability, dynamic climates, and heavy reliance on groundwater. Quantifying connections between meteoric, ground, and surface waters is critical for effective water resource management. Analyses of the stable isotopes of oxygen and hydrogen in the hydrosphere can help illuminate such connections. This study investigates the stable isotope composition of rainfall on O‘ahu in the Hawaiian Islands, with a particular focus on how altitude impacts stable isotope composition. Rainfall was sampled at 20 locations from March 2018 to August 2021. The new precipitation stable isotope data were integrated with previously published data to create the most spatially and topographically diverse precipitation collector network on O‘ahu to date. Results show that δ 18O and δ 2H values in precipitation displayed distinct isotopic signatures influenced by geographical location, season, and precipitation source. Altitude and isotopic compositions were strongly correlated along certain elevation transects, but these relationships could not be extrapolated to larger regions due to microclimate influences. Altitude and deuterium excess were strongly correlated across the study region, suggesting that deuterium excess may be a reliable proxy for precipitation elevation in local water tracer studies. Analysis of spring, rainfall, and fog stable isotope composition from Mount Ka‘ala suggests that fog may contribute up to 45% of total groundwater recharge at the summit. These findings highlight the strong influence of microclimates on the stable isotope composition of rainfall, underscore the need for further investigation into fog’s role in the water budget, and demonstrate the importance of stable isotope analysis for comprehending hydrologic dynamics in environmentally sensitive regions.
Abstract
Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts.
Significance Statement
Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.
Abstract
Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts.
Significance Statement
Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.
Abstract
Among all hydrometeorological parameters, rainfall strongly correlates with hydrometeorological disasters. The rainfall forecast process remains challenging due to the nonlinear, nonstationary nature and multiscale variability of rainfall. Moreover, the unique microclimate in different regions further complicates the forecasting process. This study proposes a hybrid model employing multivariate singular spectrum analysis (MSSA) and long short-term memory (LSTM) for multistep-ahead hourly rainfall forecasting in urban areas of northeast India. The model is trained and evaluated using high-resolution (12 km) hourly meteorological data from the Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset for Guwahati (plain) and Aizawl (hilly) regions from 2015 to 2019. The hybrid model outperforms the single LSTM model in both plain and hilly regions, with an average percentage gain of 47.99% and 43.88% for symmetric mean absolute percentage error (SMAPE) and root-mean-square error (RMSE) in the case of the Guwahati dataset and 84.59% and 82.27% in the case of the Aizawl dataset, respectively. The performance of the LSTM model significantly improves as the zero values in the observed data are eliminated after reconstruction by MSSA. This enables the model to discern essential patterns and relationships in the data, which leads to more accurate forecasts. However, the hybrid model underestimates the rainfall, which can be tackled by hypertuning the parameters. The study highlights the importance of considering the interplay between rainfall and meteorological parameters for accurate rainfall forecasting in urban areas. The proposed MSSA–LSTM model can be used as a decision support tool for urban planning and disaster management.
Significance Statement
This study addresses a critical need in multistep-ahead hourly rainfall forecasting in urban areas, with a focus on the unique conditions of northeast India. Our research has identified the presence of red noise in the rainfall data, shedding light on the complexities of the underlying rainfall patterns. Furthermore, we delve into the intricate interplay between rainfall and meteorological parameters, providing valuable insights into the factors influencing rainfall dynamics. Notably, our study underscores the region-specific challenges in rainfall forecasting. While the hybrid model demonstrates reasonable accuracy in the plains, its performance in the hilly region falls short of expectations. This highlights the nuanced nature of rainfall prediction in areas with varying topography and emphasizes the need for tailored forecasting approaches.
Abstract
Among all hydrometeorological parameters, rainfall strongly correlates with hydrometeorological disasters. The rainfall forecast process remains challenging due to the nonlinear, nonstationary nature and multiscale variability of rainfall. Moreover, the unique microclimate in different regions further complicates the forecasting process. This study proposes a hybrid model employing multivariate singular spectrum analysis (MSSA) and long short-term memory (LSTM) for multistep-ahead hourly rainfall forecasting in urban areas of northeast India. The model is trained and evaluated using high-resolution (12 km) hourly meteorological data from the Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset for Guwahati (plain) and Aizawl (hilly) regions from 2015 to 2019. The hybrid model outperforms the single LSTM model in both plain and hilly regions, with an average percentage gain of 47.99% and 43.88% for symmetric mean absolute percentage error (SMAPE) and root-mean-square error (RMSE) in the case of the Guwahati dataset and 84.59% and 82.27% in the case of the Aizawl dataset, respectively. The performance of the LSTM model significantly improves as the zero values in the observed data are eliminated after reconstruction by MSSA. This enables the model to discern essential patterns and relationships in the data, which leads to more accurate forecasts. However, the hybrid model underestimates the rainfall, which can be tackled by hypertuning the parameters. The study highlights the importance of considering the interplay between rainfall and meteorological parameters for accurate rainfall forecasting in urban areas. The proposed MSSA–LSTM model can be used as a decision support tool for urban planning and disaster management.
Significance Statement
This study addresses a critical need in multistep-ahead hourly rainfall forecasting in urban areas, with a focus on the unique conditions of northeast India. Our research has identified the presence of red noise in the rainfall data, shedding light on the complexities of the underlying rainfall patterns. Furthermore, we delve into the intricate interplay between rainfall and meteorological parameters, providing valuable insights into the factors influencing rainfall dynamics. Notably, our study underscores the region-specific challenges in rainfall forecasting. While the hybrid model demonstrates reasonable accuracy in the plains, its performance in the hilly region falls short of expectations. This highlights the nuanced nature of rainfall prediction in areas with varying topography and emphasizes the need for tailored forecasting approaches.
Abstract
Ensemble copula coupling (Schefzik et al.) is a widely used method to produce a calibrated ensemble from a calibrated probabilistic forecast. This process improves the statistical accuracy of the ensemble; in other words, the distribution of the calibrated ensemble members at each grid point more closely approximates the true expected distribution. However, the trade-off is that the individual members are often less physically realistic than the original ensemble: there is noisy variation among neighboring grid points, and, depending on the calibration method, extremes in the original ensemble are sometimes muted. We introduce neighborhood ensemble copula coupling (N-ECC), a simple modification of ECC designed to mitigate these problems. We show that, when used with the calibrated forecasts produced by Flowerdew’s (Flowerdew) reliability calibration, N-ECC improves both the visual plausibility and the statistical properties of the forecast.
Significance Statement
Numerical weather prediction (NWP) uses physical models of the atmosphere to produce a set of scenarios (called an ensemble) describing possible weather outcomes. These forecasts are used in other models to produce weather forecasts and warnings of extreme events. For example, NWP forecasts of rainfall are used in hydrological models to predict the probability of flooding. However, the raw NWP forecasts require statistical postprocessing to ensure that the range of scenarios they describe accurately represents the true range of possible outcomes. This paper introduces a new method of processing NWP forecasts to produce physically realistic, well-calibrated ensembles.
Abstract
Ensemble copula coupling (Schefzik et al.) is a widely used method to produce a calibrated ensemble from a calibrated probabilistic forecast. This process improves the statistical accuracy of the ensemble; in other words, the distribution of the calibrated ensemble members at each grid point more closely approximates the true expected distribution. However, the trade-off is that the individual members are often less physically realistic than the original ensemble: there is noisy variation among neighboring grid points, and, depending on the calibration method, extremes in the original ensemble are sometimes muted. We introduce neighborhood ensemble copula coupling (N-ECC), a simple modification of ECC designed to mitigate these problems. We show that, when used with the calibrated forecasts produced by Flowerdew’s (Flowerdew) reliability calibration, N-ECC improves both the visual plausibility and the statistical properties of the forecast.
Significance Statement
Numerical weather prediction (NWP) uses physical models of the atmosphere to produce a set of scenarios (called an ensemble) describing possible weather outcomes. These forecasts are used in other models to produce weather forecasts and warnings of extreme events. For example, NWP forecasts of rainfall are used in hydrological models to predict the probability of flooding. However, the raw NWP forecasts require statistical postprocessing to ensure that the range of scenarios they describe accurately represents the true range of possible outcomes. This paper introduces a new method of processing NWP forecasts to produce physically realistic, well-calibrated ensembles.
Abstract
Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.
Abstract
Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.
Abstract
A new pan-European climatological dataset was recently released that has a much higher spatiotemporal resolution than existing pan-European interpolated rain gauge datasets. This radar dataset of hourly precipitation accumulations, European Radar Climatology (EURADCLIM) (Overeem et al.), covers most of continental Europe with a resolution of 2 km × 2 km and is adjusted employing data from potentially thousands of government rain gauges. This study aims to use this dataset to evaluate two important satellite-derived precipitation products over the period 2013–19 at a much higher spatiotemporal resolution than was previously possible at the European scale: the IMERG late run and the Meteosat Second Generation (MSG) cloud physical property product from the SEVIRI instrument. The latter is only available during daytime, so the analyses are restricted to daytime conditions. A direct gridcell comparison of hourly precipitation reveals an apparently low coefficient of correlation. However, looking into slightly more detail at statistics pertaining to longer time scales or specific areas, the datasets show good correspondence. All datasets are shown to have their specific biases, which can be transient or more systematic, depending on the timing or location. The MSG precipitation seems to have an overall positive bias, and the IMERG dataset suffers from some transient overestimation of certain events.
Abstract
A new pan-European climatological dataset was recently released that has a much higher spatiotemporal resolution than existing pan-European interpolated rain gauge datasets. This radar dataset of hourly precipitation accumulations, European Radar Climatology (EURADCLIM) (Overeem et al.), covers most of continental Europe with a resolution of 2 km × 2 km and is adjusted employing data from potentially thousands of government rain gauges. This study aims to use this dataset to evaluate two important satellite-derived precipitation products over the period 2013–19 at a much higher spatiotemporal resolution than was previously possible at the European scale: the IMERG late run and the Meteosat Second Generation (MSG) cloud physical property product from the SEVIRI instrument. The latter is only available during daytime, so the analyses are restricted to daytime conditions. A direct gridcell comparison of hourly precipitation reveals an apparently low coefficient of correlation. However, looking into slightly more detail at statistics pertaining to longer time scales or specific areas, the datasets show good correspondence. All datasets are shown to have their specific biases, which can be transient or more systematic, depending on the timing or location. The MSG precipitation seems to have an overall positive bias, and the IMERG dataset suffers from some transient overestimation of certain events.
Abstract
We analyzed regional patterns of day-to-day precipitation variability across Europe and assessed their future changes using Coordinated Regional Climate Downscaling Experiment (CORDEX) regional climate models. A discrete Markov chain process was employed to calculate transition probabilities from wet and dry states, and the precipitation variability was quantified using the proposed variability index (I VAR, the sum of wet-to-dry and dry-to-wet transitions divided by the total number of transitions). The I VAR is, in general, lowest in southern Europe and gradually increases northward in the observed data. Performance of the regional climate models is season dependent: They capture I VAR reasonably well in summer, but higher simulated variability was found for the winter season. The I VAR trends computed for the 2006–95 period suggest decreasing day-to-day precipitation variability over southern Europe, especially in summer under the high-concentration RCP8.5 pathway. By contrast, increased variability is projected in northern Europe. Between these two regions, future I VAR trends are less clear because they strongly depend on the selection of driving global model, hinting of an uncertain future hydroclimate in the central European region.
Significance Statement
In a warming world, water availability will play a key role in ecosystem productivity. Although future changes in rainfall amounts have been studied extensively, much less attention has been given to changes in their temporal distribution and variability. Because grouping wet or dry days into sequences vitally contributes to characterizing floods or droughts, we aimed to study future changes in these tendencies. We found that although future changes in wet or dry days grouping tendencies are mostly driven solely by change in their frequency, climate models do not agree on the change in the frequency of wet days over large parts of continental Europe. This leaves major uncertainties in a future European hydroclimate and implications for impact modeling.
Abstract
We analyzed regional patterns of day-to-day precipitation variability across Europe and assessed their future changes using Coordinated Regional Climate Downscaling Experiment (CORDEX) regional climate models. A discrete Markov chain process was employed to calculate transition probabilities from wet and dry states, and the precipitation variability was quantified using the proposed variability index (I VAR, the sum of wet-to-dry and dry-to-wet transitions divided by the total number of transitions). The I VAR is, in general, lowest in southern Europe and gradually increases northward in the observed data. Performance of the regional climate models is season dependent: They capture I VAR reasonably well in summer, but higher simulated variability was found for the winter season. The I VAR trends computed for the 2006–95 period suggest decreasing day-to-day precipitation variability over southern Europe, especially in summer under the high-concentration RCP8.5 pathway. By contrast, increased variability is projected in northern Europe. Between these two regions, future I VAR trends are less clear because they strongly depend on the selection of driving global model, hinting of an uncertain future hydroclimate in the central European region.
Significance Statement
In a warming world, water availability will play a key role in ecosystem productivity. Although future changes in rainfall amounts have been studied extensively, much less attention has been given to changes in their temporal distribution and variability. Because grouping wet or dry days into sequences vitally contributes to characterizing floods or droughts, we aimed to study future changes in these tendencies. We found that although future changes in wet or dry days grouping tendencies are mostly driven solely by change in their frequency, climate models do not agree on the change in the frequency of wet days over large parts of continental Europe. This leaves major uncertainties in a future European hydroclimate and implications for impact modeling.
