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Abstract
Models have historically been the source of global soil moisture (SM) analyses and estimates of land–atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land–atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physically based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with in situ observations. When the original and timely adjusted SM products are evaluated against ground-based SM measurements over the conterminous United States, Europe, and Australia, results show the adjusted SM has significantly improved subseasonal variability. The skill of the adjusted SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM) and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. The time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.
Abstract
Models have historically been the source of global soil moisture (SM) analyses and estimates of land–atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land–atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physically based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with in situ observations. When the original and timely adjusted SM products are evaluated against ground-based SM measurements over the conterminous United States, Europe, and Australia, results show the adjusted SM has significantly improved subseasonal variability. The skill of the adjusted SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM) and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. The time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.
Abstract
Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention-based convolutional long short-term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers, spatial compression, axial attention, and encoder–decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from the Soil Moisture Active Passive L4 product at 18-km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 h ahead SM with mean R 2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes.
Abstract
Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention-based convolutional long short-term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers, spatial compression, axial attention, and encoder–decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from the Soil Moisture Active Passive L4 product at 18-km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 h ahead SM with mean R 2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes.
Abstract
A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (Rain for Peru and Ecuador), at 0.1° spatial resolution for the period 1981–2015 covering Peru and Ecuador. It is based on the application of 1) the random forest method to merge multisource precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and 2) observed and modeled streamflow data to first detect biases and second further adjust gridded precipitation by inversely applying the simulated results of the ecohydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Pacific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low, high, and peak flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.
Significance Statement
We developed a novel precipitation dataset RAIN4PE for Peru and Ecuador by merging multisource precipitation data (satellite, reanalysis, and ground-based precipitation) with terrain elevation using the random forest method. Furthermore, RAIN4PE was hydrologically corrected using streamflow data in watersheds with precipitation underestimation through reverse hydrology. The results of a comprehensive hydrological evaluation showed that RAIN4PE outperformed state-of-the-art precipitation datasets such as CHIRP, ERA5, CHIRPS, MSWEP, and PISCO in terms of daily and monthly streamflow simulations, including extremely low and high flows in almost all Peruvian and Ecuadorian catchments. This underlines the suitability of RAIN4PE for hydrometeorological applications in this region. Furthermore, our approach for the generation of RAIN4PE can be used in other data-scarce regions.
Abstract
A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (Rain for Peru and Ecuador), at 0.1° spatial resolution for the period 1981–2015 covering Peru and Ecuador. It is based on the application of 1) the random forest method to merge multisource precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and 2) observed and modeled streamflow data to first detect biases and second further adjust gridded precipitation by inversely applying the simulated results of the ecohydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Pacific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low, high, and peak flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.
Significance Statement
We developed a novel precipitation dataset RAIN4PE for Peru and Ecuador by merging multisource precipitation data (satellite, reanalysis, and ground-based precipitation) with terrain elevation using the random forest method. Furthermore, RAIN4PE was hydrologically corrected using streamflow data in watersheds with precipitation underestimation through reverse hydrology. The results of a comprehensive hydrological evaluation showed that RAIN4PE outperformed state-of-the-art precipitation datasets such as CHIRP, ERA5, CHIRPS, MSWEP, and PISCO in terms of daily and monthly streamflow simulations, including extremely low and high flows in almost all Peruvian and Ecuadorian catchments. This underlines the suitability of RAIN4PE for hydrometeorological applications in this region. Furthermore, our approach for the generation of RAIN4PE can be used in other data-scarce regions.
Abstract
Land–atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land–atmosphere coupling. One of the linkages in land–atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes, which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, northeast Asia, and southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hot spot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidence for the extreme climate change accelerated by surface energy partitioning.
Abstract
Land–atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land–atmosphere coupling. One of the linkages in land–atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes, which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, northeast Asia, and southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hot spot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidence for the extreme climate change accelerated by surface energy partitioning.
Abstract
This study investigates how extreme precipitation scales with dewpoint temperature across the northeastern United States, both in the observational record (1948–2020) and in a set of downscaled climate projections in the state of Massachusetts (2006–99). Spatiotemporal relationships between dewpoint temperature and extreme precipitation are assessed, and extreme precipitation–temperature scaling rates are evaluated on annual and seasonal scales using nonstationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% °C−1, but this varies by season, with most nonzero scaling rates in summer and fall and the largest rates (∼7.3% °C−1) in the summer. Dewpoint temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between −2.5% and 6.2% °C−1 at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dewpoint temperature over the twenty-first century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% °C−1). Overall, the observations suggest that extreme daily precipitation in the Northeast only thermodynamic scales with dewpoint temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.
Significance Statement
A warmer climate will likely result in the intensification of extreme precipitation, with the potential to enhance flood and stormwater risk. However, the relationship between extreme precipitation and temperature (i.e., the precipitation–temperature scaling rate) remains uncertain, particularly at regional scales, inhibiting societal adaptation to extreme events. Using observations and climate projections of daily precipitation and dewpoint temperature across the northeastern United States, we demonstrate that extreme daily precipitation does indeed scale with dewpoint temperature, but the rate of scaling varies by season, with the strongest relationship in the warm season.
Abstract
This study investigates how extreme precipitation scales with dewpoint temperature across the northeastern United States, both in the observational record (1948–2020) and in a set of downscaled climate projections in the state of Massachusetts (2006–99). Spatiotemporal relationships between dewpoint temperature and extreme precipitation are assessed, and extreme precipitation–temperature scaling rates are evaluated on annual and seasonal scales using nonstationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% °C−1, but this varies by season, with most nonzero scaling rates in summer and fall and the largest rates (∼7.3% °C−1) in the summer. Dewpoint temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between −2.5% and 6.2% °C−1 at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dewpoint temperature over the twenty-first century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% °C−1). Overall, the observations suggest that extreme daily precipitation in the Northeast only thermodynamic scales with dewpoint temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.
Significance Statement
A warmer climate will likely result in the intensification of extreme precipitation, with the potential to enhance flood and stormwater risk. However, the relationship between extreme precipitation and temperature (i.e., the precipitation–temperature scaling rate) remains uncertain, particularly at regional scales, inhibiting societal adaptation to extreme events. Using observations and climate projections of daily precipitation and dewpoint temperature across the northeastern United States, we demonstrate that extreme daily precipitation does indeed scale with dewpoint temperature, but the rate of scaling varies by season, with the strongest relationship in the warm season.
Abstract
High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.
Significance Statement: Gridded historical climate datasets are vital inputs for hydrological and other models used to quantify current water supply, drought risk, and other ecosystem processes. They are also compared to future climate projections to assess the future climate change risk. Numerous interpolated climate datasets are available with varying resolution and methods, yet all are based on climate station data and represent the same historical record at and in between each station. We found significant disagreement between historical gridded datasets, including the one used to bias correct the current climate change projections used by the state of California. Some datasets had large biases compared to station data, especially in snow-dominated regions, leading to large disagreements in modeled monthly streamflow.
Abstract
High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.
Significance Statement: Gridded historical climate datasets are vital inputs for hydrological and other models used to quantify current water supply, drought risk, and other ecosystem processes. They are also compared to future climate projections to assess the future climate change risk. Numerous interpolated climate datasets are available with varying resolution and methods, yet all are based on climate station data and represent the same historical record at and in between each station. We found significant disagreement between historical gridded datasets, including the one used to bias correct the current climate change projections used by the state of California. Some datasets had large biases compared to station data, especially in snow-dominated regions, leading to large disagreements in modeled monthly streamflow.
Abstract
Soil temperature (ST) is one of the key variables in land–atmosphere interactions. The response of ST to atmospheric changes and subsequent influence of ST on atmosphere can be recognized as the processes of signals propagation. Understanding the storing and releasing of atmospheric signals in ST favors the improvement of climate prediction and weather forecast. However, the current understanding of the lagging response of ST to atmospheric changes is very insufficient. The analysis based on observation shows that both the storage of air temperature signals in deep ST even after 4 months and the storage of precipitation signals in shallow ST after 1 month are widespread phenomena in China. Air temperature signals at 2 m can propagate to the soil depths of 160 and 320 cm after 1 and 2 months, respectively. The storages of antecedent air temperature and precipitation signals in ST are slightly weaker and stronger during April–September, respectively, which is related to more precipitation during growing season. The precipitation signals in ST rapidly weaken after 2 months. Moreover, the effects of accumulated precipitation and air temperature on the signal storage in ST have significant monthly variations and vary linearly with soil depth and latitude. The storage of antecedent air temperature or precipitation signals in ST exhibits an obvious decadal variation with a period of more than 50 years, and it may result from the modulation of the global climate patterns which largely affect local air temperature and precipitation.
Abstract
Soil temperature (ST) is one of the key variables in land–atmosphere interactions. The response of ST to atmospheric changes and subsequent influence of ST on atmosphere can be recognized as the processes of signals propagation. Understanding the storing and releasing of atmospheric signals in ST favors the improvement of climate prediction and weather forecast. However, the current understanding of the lagging response of ST to atmospheric changes is very insufficient. The analysis based on observation shows that both the storage of air temperature signals in deep ST even after 4 months and the storage of precipitation signals in shallow ST after 1 month are widespread phenomena in China. Air temperature signals at 2 m can propagate to the soil depths of 160 and 320 cm after 1 and 2 months, respectively. The storages of antecedent air temperature and precipitation signals in ST are slightly weaker and stronger during April–September, respectively, which is related to more precipitation during growing season. The precipitation signals in ST rapidly weaken after 2 months. Moreover, the effects of accumulated precipitation and air temperature on the signal storage in ST have significant monthly variations and vary linearly with soil depth and latitude. The storage of antecedent air temperature or precipitation signals in ST exhibits an obvious decadal variation with a period of more than 50 years, and it may result from the modulation of the global climate patterns which largely affect local air temperature and precipitation.
Abstract
In this study, we identified the origin of the moisture associated with the tropical cyclones’ (TCs) precipitation in the North Atlantic Ocean basin during their three well-differentiated life stages between 1980 and 2018. The HURDAT2 database was used to detect the location of 598 TCs during their genesis, maximum intensification peak, and dissipation phases. The global outputs of the Lagrangian FLEXPART model were then used to determine the moisture sources. Using a k-means cluster analysis technique, seven different regions were identified as the most common locations for the genesis and maximum intensity of the TC phases, while six regions were found for the dissipation points. Our results showed that the origin of moisture precipitating was not entirely local over the areas of TC occurrence. The North Atlantic Ocean to the north of the intertropical convergence zone at 10°N (NATL)—especially from tropical latitudes, the Caribbean Sea, and the Gulf of Mexico—provides most of the moisture for TCs (∼87%). The Atlantic Ocean basin southward of the ITCZ (SATL) played a nonnegligible role (∼11%), with its contribution being most pronounced during the TC genesis phase, while the eastern tropical Pacific Ocean made the smallest contribution (∼2%). The moisture supported by TCs varied depending on their category, being higher for hurricanes than for major hurricanes or tropical storms. Additionally, the approach permitted the estimation of the mean residence time of the water vapor uptake that produces the precipitation during TC activity, which ranged between 2.6 and 2.9 days.
Significance Statement
Atmospheric moisture transport plays an important role in the genesis and intensification of tropical cyclones (TCs). In this study, we investigated the moisture source for the genesis, intensification, and dissipation of TCs in the North Atlantic Ocean basin using a Lagrangian approach. This model allowed us to track air masses backward in time from the target area to identify regions where air masses experienced an uptake of moisture prior to reaching the area of interest. The sources were identified individually for each TC, and the results were then combined to provide a broad general picture with some surprising outstanding results, such as the role of the North and South Atlantic and the eastern tropical Pacific as important moisture sources during the different TCs phases and intensities.
Abstract
In this study, we identified the origin of the moisture associated with the tropical cyclones’ (TCs) precipitation in the North Atlantic Ocean basin during their three well-differentiated life stages between 1980 and 2018. The HURDAT2 database was used to detect the location of 598 TCs during their genesis, maximum intensification peak, and dissipation phases. The global outputs of the Lagrangian FLEXPART model were then used to determine the moisture sources. Using a k-means cluster analysis technique, seven different regions were identified as the most common locations for the genesis and maximum intensity of the TC phases, while six regions were found for the dissipation points. Our results showed that the origin of moisture precipitating was not entirely local over the areas of TC occurrence. The North Atlantic Ocean to the north of the intertropical convergence zone at 10°N (NATL)—especially from tropical latitudes, the Caribbean Sea, and the Gulf of Mexico—provides most of the moisture for TCs (∼87%). The Atlantic Ocean basin southward of the ITCZ (SATL) played a nonnegligible role (∼11%), with its contribution being most pronounced during the TC genesis phase, while the eastern tropical Pacific Ocean made the smallest contribution (∼2%). The moisture supported by TCs varied depending on their category, being higher for hurricanes than for major hurricanes or tropical storms. Additionally, the approach permitted the estimation of the mean residence time of the water vapor uptake that produces the precipitation during TC activity, which ranged between 2.6 and 2.9 days.
Significance Statement
Atmospheric moisture transport plays an important role in the genesis and intensification of tropical cyclones (TCs). In this study, we investigated the moisture source for the genesis, intensification, and dissipation of TCs in the North Atlantic Ocean basin using a Lagrangian approach. This model allowed us to track air masses backward in time from the target area to identify regions where air masses experienced an uptake of moisture prior to reaching the area of interest. The sources were identified individually for each TC, and the results were then combined to provide a broad general picture with some surprising outstanding results, such as the role of the North and South Atlantic and the eastern tropical Pacific as important moisture sources during the different TCs phases and intensities.
Abstract
Recent years have seen growing appreciation that rapidly intensifying flash droughts are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility–based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture flash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.
Abstract
Recent years have seen growing appreciation that rapidly intensifying flash droughts are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility–based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture flash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.
Abstract
The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multisource precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered from −1 to 0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1, and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm , Nw ). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error.
Significance Statement
This study first compares the total and different rain types of near-surface rainfall measured by DPR and ground-based radar CDP, then separates the error of DPR near-surface rainfall into systematic and random errors and analyzes the possible causes of the error. The purpose of this study is to better apply the error model to applications such as optimal data fusion and hydrological modeling, and the analysis of the error can also provide a basis for improving the spaceborne precipitation retrieval algorithm.
Abstract
The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multisource precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered from −1 to 0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1, and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm , Nw ). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error.
Significance Statement
This study first compares the total and different rain types of near-surface rainfall measured by DPR and ground-based radar CDP, then separates the error of DPR near-surface rainfall into systematic and random errors and analyzes the possible causes of the error. The purpose of this study is to better apply the error model to applications such as optimal data fusion and hydrological modeling, and the analysis of the error can also provide a basis for improving the spaceborne precipitation retrieval algorithm.
