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Abstract
Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales–Atmosphere (MPAS-A), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1–2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3–4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts.
Significance Statement
Snow cover is a crucial variable in hydrometeorology. Subseasonal forecasting, which involves predicting snow cover anomalies 1–4 weeks in advance, has socioeconomic value. We conducted an evaluation of the subseasonal forecasts for Northern Hemisphere winter snow cover produced by three numerical models. This evaluation provides insights into the accuracy and reliability of these models, which could contribute to their enhancement. Furthermore, we examined the impact of the predicted snow cover on the skill of surface air temperature subseasonal forecasts. The results suggest that improvements in snow cover modeling and forecasting can lead to more accurate subseasonal atmospheric forecasts. Therefore, future efforts to refine snow cover parameterization schemes suitable for subseasonal forecasting are promising and worthwhile.
Abstract
Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales–Atmosphere (MPAS-A), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1–2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3–4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts.
Significance Statement
Snow cover is a crucial variable in hydrometeorology. Subseasonal forecasting, which involves predicting snow cover anomalies 1–4 weeks in advance, has socioeconomic value. We conducted an evaluation of the subseasonal forecasts for Northern Hemisphere winter snow cover produced by three numerical models. This evaluation provides insights into the accuracy and reliability of these models, which could contribute to their enhancement. Furthermore, we examined the impact of the predicted snow cover on the skill of surface air temperature subseasonal forecasts. The results suggest that improvements in snow cover modeling and forecasting can lead to more accurate subseasonal atmospheric forecasts. Therefore, future efforts to refine snow cover parameterization schemes suitable for subseasonal forecasting are promising and worthwhile.
Abstract
Flash droughts (FDs) have attracted widespread attention in recent years due to their sudden onset and rapid intensification with significant impacts on ecosystems, water resources, and agriculture. These features of FDs pose unique challenges for their forecast, monitoring, and mitigation. The impact of FDs on society can vary depending on several factors, such as the frequency of their occurrence, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study developed a novel approach to quantitatively define FD based on the aridity index. This new approach was used to examine spatiotemporal characteristics (including trends) and triggers of FDs over 25 river basins across India from 1981 to 2021. The hydrometeorological conditions, including soil moisture percentiles, anomalies of precipitation, temperature, and vapor pressure deficit were investigated at different stages of FD. Results suggest that FDs with high intensification rates are more common in humid areas compared to subhumid and semiarid areas. Both precipitation and temperature are primary triggers of FDs over a major part of the study area. The individual effects of soil moisture and precipitation also act as a trigger across some regions (like northeast India and the Western Ghats). Additionally, atmospheric aridity can create conditions conducive to FDs, and when combined with depleted soil moisture, it can accelerate their rapid onset. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Significance Statement
Flash droughts have attracted widespread attention due to their sudden onset and rapid intensification with significant impacts on multiple vectors. The impact of flash drought on society depends on their frequency, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study develops a novel approach to quantitatively define flash drought based on the aridity index. This new approach is used to examine spatiotemporal characteristics and triggers of flash drought over 25 river basins across India from 1981 to 2021. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Abstract
Flash droughts (FDs) have attracted widespread attention in recent years due to their sudden onset and rapid intensification with significant impacts on ecosystems, water resources, and agriculture. These features of FDs pose unique challenges for their forecast, monitoring, and mitigation. The impact of FDs on society can vary depending on several factors, such as the frequency of their occurrence, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study developed a novel approach to quantitatively define FD based on the aridity index. This new approach was used to examine spatiotemporal characteristics (including trends) and triggers of FDs over 25 river basins across India from 1981 to 2021. The hydrometeorological conditions, including soil moisture percentiles, anomalies of precipitation, temperature, and vapor pressure deficit were investigated at different stages of FD. Results suggest that FDs with high intensification rates are more common in humid areas compared to subhumid and semiarid areas. Both precipitation and temperature are primary triggers of FDs over a major part of the study area. The individual effects of soil moisture and precipitation also act as a trigger across some regions (like northeast India and the Western Ghats). Additionally, atmospheric aridity can create conditions conducive to FDs, and when combined with depleted soil moisture, it can accelerate their rapid onset. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Significance Statement
Flash droughts have attracted widespread attention due to their sudden onset and rapid intensification with significant impacts on multiple vectors. The impact of flash drought on society depends on their frequency, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study develops a novel approach to quantitatively define flash drought based on the aridity index. This new approach is used to examine spatiotemporal characteristics and triggers of flash drought over 25 river basins across India from 1981 to 2021. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Abstract
The northeasterly facing, windward side of the Island of Kaua‘i (part of the State of Hawai‘i, United States) is prone to heavy rainfall events due to its topographical features and geographical location. Persistent northeasterly trade winds, coupled with steep changes in elevation, create an ideal environment for orographic precipitation. In addition, due to Kaua‘i’s 22°N latitude, the island often experiences midlatitude weather features such as kona lows, upper-level lows, and cold fronts that frequently result in high rainfall and river discharge conditions. This work uses data from river gauges in Halele‘a to understand the seasonality and impacts of the main atmospheric disturbances on two rivers in the region. The seasonality study showed that the majority of extreme flooding events occurred during the cool season and were predominantly caused by cold fronts and upper-level troughs. The historical analysis used atmospheric disturbance cases to determine that kona lows were likely to cause high streamflow in both studied Halele‘a rivers, and upper-level lows had an approximately equal probability of causing high streamflow or not. The findings that come from this project can provide context to atmospheric disturbances in Halele‘a and help community members identify and anticipate the types of events that may contribute to flooding.
Significance Statement
The north shore of the Island of Kaua‘i is prone to extreme rainfall and flooding due to interactions between the typical wind patterns and the nearby mountain range. A majority of flooding events occur during the cool season (October–April) because most of the weather events that produce extreme rainfall occur during these months. Here, we examined the top 50 flooding events in two of Kaua‘i’s north shore rivers and found that cold fronts and upper-level low pressure systems are often responsible for flooding. Additionally, any kona low is likely to cause high streamflow. This study increases the understanding of flood causes and likelihood in northern Kaua‘i.
Abstract
The northeasterly facing, windward side of the Island of Kaua‘i (part of the State of Hawai‘i, United States) is prone to heavy rainfall events due to its topographical features and geographical location. Persistent northeasterly trade winds, coupled with steep changes in elevation, create an ideal environment for orographic precipitation. In addition, due to Kaua‘i’s 22°N latitude, the island often experiences midlatitude weather features such as kona lows, upper-level lows, and cold fronts that frequently result in high rainfall and river discharge conditions. This work uses data from river gauges in Halele‘a to understand the seasonality and impacts of the main atmospheric disturbances on two rivers in the region. The seasonality study showed that the majority of extreme flooding events occurred during the cool season and were predominantly caused by cold fronts and upper-level troughs. The historical analysis used atmospheric disturbance cases to determine that kona lows were likely to cause high streamflow in both studied Halele‘a rivers, and upper-level lows had an approximately equal probability of causing high streamflow or not. The findings that come from this project can provide context to atmospheric disturbances in Halele‘a and help community members identify and anticipate the types of events that may contribute to flooding.
Significance Statement
The north shore of the Island of Kaua‘i is prone to extreme rainfall and flooding due to interactions between the typical wind patterns and the nearby mountain range. A majority of flooding events occur during the cool season (October–April) because most of the weather events that produce extreme rainfall occur during these months. Here, we examined the top 50 flooding events in two of Kaua‘i’s north shore rivers and found that cold fronts and upper-level low pressure systems are often responsible for flooding. Additionally, any kona low is likely to cause high streamflow. This study increases the understanding of flood causes and likelihood in northern Kaua‘i.
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.
