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Abstract
The warm season in the United States Great Plains (GP) is characterized by frequent nocturnal low-level jets (LLJs). The GPLLJ serves as a major mechanism of atmospheric moisture transport, contributing to severe weather and precipitation in the region. A combination of synoptic and regional forcing modulates GPLLJ frequency and intensity. The GPLLJ has primarily been studied at the diurnal scale. We hypothesize that, due to the memory of the land surface, longer time scale variability associated with surface moisture also modulates GPLLJ intensity. This work identifies GPLLJ days from ECMWF Reanalysis v5 (ERA5) wind data and isolates extremes using a peaks-over-threshold approach. Extreme GPLLJs are classified by geographic region and synoptic state. Composites of daily soil moisture anomalies show a preference for extreme GPLLJs to occur over anomalously dry soil. Critically, antecedent soil moisture anomalies emerge weeks before the extreme jet occurrence. The dry soil moisture signal coexists with clear skies and drying of the surface at the synoptic time scale. A diurnal PBL heat accumulation, which intensifies the buoyancy oscillation, is also present. The identification of a subseasonal dry anomaly suggests that, although the GPLLJ is generated by diurnally varying oscillations and intensified by synoptic-scale processes, the memory of the land surface can modulate the GPLLJ far beyond the diurnal and synoptic scale. Additionally, the location of the antecedent soil moisture anomalies corresponds with the eventual GPLLJ. The spatiotemporal characteristic of these antecedent anomalies suggests the potential for improved prediction of the GPLLJ activity.
Abstract
The warm season in the United States Great Plains (GP) is characterized by frequent nocturnal low-level jets (LLJs). The GPLLJ serves as a major mechanism of atmospheric moisture transport, contributing to severe weather and precipitation in the region. A combination of synoptic and regional forcing modulates GPLLJ frequency and intensity. The GPLLJ has primarily been studied at the diurnal scale. We hypothesize that, due to the memory of the land surface, longer time scale variability associated with surface moisture also modulates GPLLJ intensity. This work identifies GPLLJ days from ECMWF Reanalysis v5 (ERA5) wind data and isolates extremes using a peaks-over-threshold approach. Extreme GPLLJs are classified by geographic region and synoptic state. Composites of daily soil moisture anomalies show a preference for extreme GPLLJs to occur over anomalously dry soil. Critically, antecedent soil moisture anomalies emerge weeks before the extreme jet occurrence. The dry soil moisture signal coexists with clear skies and drying of the surface at the synoptic time scale. A diurnal PBL heat accumulation, which intensifies the buoyancy oscillation, is also present. The identification of a subseasonal dry anomaly suggests that, although the GPLLJ is generated by diurnally varying oscillations and intensified by synoptic-scale processes, the memory of the land surface can modulate the GPLLJ far beyond the diurnal and synoptic scale. Additionally, the location of the antecedent soil moisture anomalies corresponds with the eventual GPLLJ. The spatiotemporal characteristic of these antecedent anomalies suggests the potential for improved prediction of the GPLLJ activity.
Abstract
Precipitation is a vital process in the water cycle. Accurate estimation of the precipitation rate underpins the success of hydrological simulations, flood predictions, and water resource management. Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation inversion. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness–rain rate relationship. To resolve these problems, we construct a deep learning model using a spherical convolutional neural network to properly represent Earth’s spherical surface. With data input directly from IR bands 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), our new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained and tested with a 3-month-long dataset, and then validated in a 2-yr period. Compared to the commonly used IR-based precipitation product PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE, and CC, especially in the dry region and for extreme rainfall events. Decomposed with the four-component error decomposition (4CED) method, the overestimation of PEISCNN was averaged 47.66% lower than the CCS at the hourly scale. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications.
Significance Statement
An IR-based precipitation algorithm is irreplaceable in satellite precipitation inversion, since an IR sensor can provide observations of high frequency, fine temporal resolution, and wide coverage. Considering the spherical nature of Earth’s surface which has been overlooked in previous IR-based precipitation retrieval algorithms, we proposed a new deep learning model PEISCNN, which can address the problems that exist in IR-based precipitation estimations such as overestimation in dry regions, deficiency in extreme rainfall events, and reliance on the empirical cloud-top brightness–rain rate relationship. PEISCNN provides a new insight to improve the accuracy of the satellite IR-based or multisensor-based precipitation estimation, and it has great potential to benefit a range of related hydrological research, applications in water resource management, and flood predictions.
Abstract
Precipitation is a vital process in the water cycle. Accurate estimation of the precipitation rate underpins the success of hydrological simulations, flood predictions, and water resource management. Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation inversion. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness–rain rate relationship. To resolve these problems, we construct a deep learning model using a spherical convolutional neural network to properly represent Earth’s spherical surface. With data input directly from IR bands 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), our new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained and tested with a 3-month-long dataset, and then validated in a 2-yr period. Compared to the commonly used IR-based precipitation product PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE, and CC, especially in the dry region and for extreme rainfall events. Decomposed with the four-component error decomposition (4CED) method, the overestimation of PEISCNN was averaged 47.66% lower than the CCS at the hourly scale. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications.
Significance Statement
An IR-based precipitation algorithm is irreplaceable in satellite precipitation inversion, since an IR sensor can provide observations of high frequency, fine temporal resolution, and wide coverage. Considering the spherical nature of Earth’s surface which has been overlooked in previous IR-based precipitation retrieval algorithms, we proposed a new deep learning model PEISCNN, which can address the problems that exist in IR-based precipitation estimations such as overestimation in dry regions, deficiency in extreme rainfall events, and reliance on the empirical cloud-top brightness–rain rate relationship. PEISCNN provides a new insight to improve the accuracy of the satellite IR-based or multisensor-based precipitation estimation, and it has great potential to benefit a range of related hydrological research, applications in water resource management, and flood predictions.
Abstract
The surface precipitation network in Canada suffers from large data gaps due to the challenge of covering a large country with a low population density. A proof-of-concept for an optimal network design is proposed to more efficiently estimate precipitation in Canada with the design goal of minimizing the interpolation uncertainty. The network design is based on a statistical model of precipitation that accounts for intermittency and non-Gaussianity of precipitation. Our results indicate that the greatest needs for new stations are in British Columbia, where coastal and mountain climate leads to more uncertainty in precipitation amounts, while the Prairie Provinces (Alberta, Saskatchewan, and Manitoba) could gain efficiencies by reducing their network size. Despite the current low density of stations in the territories north of Canada, these drier and colder regions only have a moderate need for more stations, mostly in the mountainous regions of Yukon. However, from a spatially varying wind undercatch measurement error model, it is shown that these northern regions have greatest need for higher-accuracy measurements.
Significance Statement
The proposed methodology can guide in the optimal placement of precipitation gauges across a large country such as Canada, which will provide value for money in how rain and snow are monitored.
Abstract
The surface precipitation network in Canada suffers from large data gaps due to the challenge of covering a large country with a low population density. A proof-of-concept for an optimal network design is proposed to more efficiently estimate precipitation in Canada with the design goal of minimizing the interpolation uncertainty. The network design is based on a statistical model of precipitation that accounts for intermittency and non-Gaussianity of precipitation. Our results indicate that the greatest needs for new stations are in British Columbia, where coastal and mountain climate leads to more uncertainty in precipitation amounts, while the Prairie Provinces (Alberta, Saskatchewan, and Manitoba) could gain efficiencies by reducing their network size. Despite the current low density of stations in the territories north of Canada, these drier and colder regions only have a moderate need for more stations, mostly in the mountainous regions of Yukon. However, from a spatially varying wind undercatch measurement error model, it is shown that these northern regions have greatest need for higher-accuracy measurements.
Significance Statement
The proposed methodology can guide in the optimal placement of precipitation gauges across a large country such as Canada, which will provide value for money in how rain and snow are monitored.
Abstract
Predictions of drought onset and termination at subseasonal (from 2 weeks to 1 month) lead times could provide a foundation for more effective and proactive drought management. We used reforecasts archived in NOAA’s Subseasonal Experiment (SubX) to force the Noah Multiparameterization (Noah-MP), which produced forecasts of soil moisture from which we identified drought levels D0–D4. We evaluated forecast skill of major and more modest droughts, with leads from 1 to 4 weeks, and with particular attention to drought termination and onset. We find usable drought termination and onset forecast skill at leads 1 and 2 weeks for major D0–D2 droughts and limited skill at week 3 for major D0–D1 droughts, with essentially no skill at week 4 regardless of drought severity. Furthermore, for both major and more modest droughts, we find limited skill or no skill for D3–D4 droughts. We find that skill is generally higher for drought termination than for onset for all drought events. We also find that drought prediction skill generally decreases from north to south for all drought events.
Abstract
Predictions of drought onset and termination at subseasonal (from 2 weeks to 1 month) lead times could provide a foundation for more effective and proactive drought management. We used reforecasts archived in NOAA’s Subseasonal Experiment (SubX) to force the Noah Multiparameterization (Noah-MP), which produced forecasts of soil moisture from which we identified drought levels D0–D4. We evaluated forecast skill of major and more modest droughts, with leads from 1 to 4 weeks, and with particular attention to drought termination and onset. We find usable drought termination and onset forecast skill at leads 1 and 2 weeks for major D0–D2 droughts and limited skill at week 3 for major D0–D1 droughts, with essentially no skill at week 4 regardless of drought severity. Furthermore, for both major and more modest droughts, we find limited skill or no skill for D3–D4 droughts. We find that skill is generally higher for drought termination than for onset for all drought events. We also find that drought prediction skill generally decreases from north to south for all drought events.
Abstract
Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.
Abstract
Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.
Abstract
Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25% between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well.
Significance Statement
We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals.
Abstract
Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25% between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well.
Significance Statement
We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals.
Abstract
In the postprocessing of ensemble forecasts of weather variables, it is standard practice to first calibrate the forecasts in a univariate setting, before reconstructing multivariate ensembles that have a correct covariability in space, time, and across variables, via so-called “reordering” methods. Within this framework though, postprocessors cannot fully extract the skill of the raw forecast that may exist at larger scales. A multi-temporal-scale modulation mechanism for precipitation is here presented, which aims at improving the forecasts over different accumulation periods, and which can be coupled with any univariate calibration and multivariate reordering techniques. The idea, originally known under the term “canonical events,” has been implemented for more than a decade in the Meteorological Ensemble Forecast Processor (MEFP), a component of the U.S. National Weather Service’s (NWS) Hydrologic Ensemble Forecast Service (HEFS), although users were left with material in the gray literature. This paper proposes a formal description of the mechanism and studies its intrinsic connection with the multivariate reordering process. The verification of modulated and unmodulated forecasts, when coupled with two popular methods for reordering, the Schaake shuffle and ensemble copula coupling (ECC), is performed on 11 Californian basins, on both precipitation and streamflow. Results demonstrate the clear benefit of the multi-temporal-scale modulation, in particular on multiday total streamflow. However, the relative gain depends on the method used for reordering, with more benefits expected when this latter method is not able to reconstruct an adequate temporal structure on the calibrated precipitation forecasts.
Abstract
In the postprocessing of ensemble forecasts of weather variables, it is standard practice to first calibrate the forecasts in a univariate setting, before reconstructing multivariate ensembles that have a correct covariability in space, time, and across variables, via so-called “reordering” methods. Within this framework though, postprocessors cannot fully extract the skill of the raw forecast that may exist at larger scales. A multi-temporal-scale modulation mechanism for precipitation is here presented, which aims at improving the forecasts over different accumulation periods, and which can be coupled with any univariate calibration and multivariate reordering techniques. The idea, originally known under the term “canonical events,” has been implemented for more than a decade in the Meteorological Ensemble Forecast Processor (MEFP), a component of the U.S. National Weather Service’s (NWS) Hydrologic Ensemble Forecast Service (HEFS), although users were left with material in the gray literature. This paper proposes a formal description of the mechanism and studies its intrinsic connection with the multivariate reordering process. The verification of modulated and unmodulated forecasts, when coupled with two popular methods for reordering, the Schaake shuffle and ensemble copula coupling (ECC), is performed on 11 Californian basins, on both precipitation and streamflow. Results demonstrate the clear benefit of the multi-temporal-scale modulation, in particular on multiday total streamflow. However, the relative gain depends on the method used for reordering, with more benefits expected when this latter method is not able to reconstruct an adequate temporal structure on the calibrated precipitation forecasts.
Abstract
The processes underlying heavy rainfall in the higher elevations of the Himalayas are still not well known despite their importance. Here, we examine the detailed process causing a heavy rainfall event, observed by our rain gauge network in the Rolwaling valley, eastern Nepal Himalayas, using ERA5 and a regional cloud-resolving numerical simulation. Heavy precipitation (112 mm day−1) was observed on 8 July 2019 at Dongang (2790 m above sea level). Most of the precipitation (81 mm) occurred during 1900–2300 local time (LT). The synoptic-scale environment is characterized by a monsoon low pressure system (LPS) over northeastern India. The LPS lifted moisture upward from the lower troposphere and then horizontally transported it into the eastern Nepal Himalayas within the middle troposphere, increasing the content of the water vapor around Dongang. A mesoscale convective system passed over Dongang around the time of the intense precipitation. The numerical simulation showed that surface heat fluxes prevailed under the middle tropospheric (∼500 hPa) southeasterly flow associated with the LPS around a mountain ridge on the upwind side of Dongang until 1900 LT, enhancing convective instability. Topographic lifting led to the release of the enhanced instability, which triggered the development of a mesoscale precipitation system. The southeasterly flow pushed the precipitation system northward, which then passed over Dongang during 2000–2200 LT, resulting in heavy precipitation. Thus, we conclude that the heavy precipitation came from the multiscale processes such as three-dimensional moisture transport driven by the LPS and the diurnal variation in heat fluxes from the land surface.
Significance Statement
Precipitation in the Himalayas is closely related to the hydrological cycle, floods, and landslide disasters in South Asia. Thus, elucidating the features of precipitation in the Himalayas is important. This study explored multiscale processes leading to a heavy precipitation event that was observed on 8 July 2019 at Dongang in the Rolwaling valley of the eastern Nepal Himalayas. We identified new processes producing heavy precipitation in the Himalayas: the three-dimensional synoptic-scale moisture transport driven by a monsoon low pressure system and the effect of the diurnal variation in heat fluxes from the land surface on the development and movement of a mesoscale precipitation system causing heavy precipitation. These findings broaden our understanding of heavy precipitation in the Himalayas.
Abstract
The processes underlying heavy rainfall in the higher elevations of the Himalayas are still not well known despite their importance. Here, we examine the detailed process causing a heavy rainfall event, observed by our rain gauge network in the Rolwaling valley, eastern Nepal Himalayas, using ERA5 and a regional cloud-resolving numerical simulation. Heavy precipitation (112 mm day−1) was observed on 8 July 2019 at Dongang (2790 m above sea level). Most of the precipitation (81 mm) occurred during 1900–2300 local time (LT). The synoptic-scale environment is characterized by a monsoon low pressure system (LPS) over northeastern India. The LPS lifted moisture upward from the lower troposphere and then horizontally transported it into the eastern Nepal Himalayas within the middle troposphere, increasing the content of the water vapor around Dongang. A mesoscale convective system passed over Dongang around the time of the intense precipitation. The numerical simulation showed that surface heat fluxes prevailed under the middle tropospheric (∼500 hPa) southeasterly flow associated with the LPS around a mountain ridge on the upwind side of Dongang until 1900 LT, enhancing convective instability. Topographic lifting led to the release of the enhanced instability, which triggered the development of a mesoscale precipitation system. The southeasterly flow pushed the precipitation system northward, which then passed over Dongang during 2000–2200 LT, resulting in heavy precipitation. Thus, we conclude that the heavy precipitation came from the multiscale processes such as three-dimensional moisture transport driven by the LPS and the diurnal variation in heat fluxes from the land surface.
Significance Statement
Precipitation in the Himalayas is closely related to the hydrological cycle, floods, and landslide disasters in South Asia. Thus, elucidating the features of precipitation in the Himalayas is important. This study explored multiscale processes leading to a heavy precipitation event that was observed on 8 July 2019 at Dongang in the Rolwaling valley of the eastern Nepal Himalayas. We identified new processes producing heavy precipitation in the Himalayas: the three-dimensional synoptic-scale moisture transport driven by a monsoon low pressure system and the effect of the diurnal variation in heat fluxes from the land surface on the development and movement of a mesoscale precipitation system causing heavy precipitation. These findings broaden our understanding of heavy precipitation in the Himalayas.
Abstract
The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing, highly localized effects. The diurnal cycle in regions such as the Amazon and the Maritime Continent are of particular interest, due to the complex coastal and terrain effects. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset are used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-yr (2000–18) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on half-hourly averaged precipitation at 0.1° × 0.1°. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broadscale patterns of diurnal variability but does not capture all the fine-scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long-record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.
Abstract
The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing, highly localized effects. The diurnal cycle in regions such as the Amazon and the Maritime Continent are of particular interest, due to the complex coastal and terrain effects. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset are used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-yr (2000–18) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on half-hourly averaged precipitation at 0.1° × 0.1°. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broadscale patterns of diurnal variability but does not capture all the fine-scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long-record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.
Abstract
This study aims to comprehend the propagation of meteorological drought [expressed by the standardized precipitation evapotranspiration index (SPEI)] into hydrological drought [expressed by the standardized runoff index (SRI)] using the combined application of principal component analysis (PCA) and wavelet analysis for a period of 39 years (1980–2018) in the Indus basin, Pakistan. PCA was used to calculate principal components of precipitation, temperature, and streamflow, which were used to systematically propagate drought from one catchment to another, resulting in a catchment-scale drought assessment. The systematic propagation of drought was useful in capturing the effects of local climate variability in the 27 catchments of the Indus basin. Wavelet analyses are used to calculate the variability of SPEI/SRI and propagation (analyzed with the wavelet coherence) from SPEI to SRI. The propagation time from SPEI to SRI was cross correlated. SPEI/SRI time series showed extreme/severe droughts in 16 out of the 39 years, where relatively weak apparent wet and drought events are observed at short periods (1 month) and apparent at longer periods (6 and 12 months). Propagation from SPEI to SRI is catchment specific, with most catchments showing transition in early years (1997–2003). Propagation rate is higher in the upper Indus basin (UIB) and lower Indus basin (LIB) than in the middle Indus basin (MIB), suggesting that climate plays an important role in drought development and propagation. Results also showed a shorter and longer propagation time in the UIB and LIB, respectively. This study has helped us understand the behavior of droughts at catchment scale and will therefore help in the development of drought mitigation plans in Pakistan and similar regions around the world.
Abstract
This study aims to comprehend the propagation of meteorological drought [expressed by the standardized precipitation evapotranspiration index (SPEI)] into hydrological drought [expressed by the standardized runoff index (SRI)] using the combined application of principal component analysis (PCA) and wavelet analysis for a period of 39 years (1980–2018) in the Indus basin, Pakistan. PCA was used to calculate principal components of precipitation, temperature, and streamflow, which were used to systematically propagate drought from one catchment to another, resulting in a catchment-scale drought assessment. The systematic propagation of drought was useful in capturing the effects of local climate variability in the 27 catchments of the Indus basin. Wavelet analyses are used to calculate the variability of SPEI/SRI and propagation (analyzed with the wavelet coherence) from SPEI to SRI. The propagation time from SPEI to SRI was cross correlated. SPEI/SRI time series showed extreme/severe droughts in 16 out of the 39 years, where relatively weak apparent wet and drought events are observed at short periods (1 month) and apparent at longer periods (6 and 12 months). Propagation from SPEI to SRI is catchment specific, with most catchments showing transition in early years (1997–2003). Propagation rate is higher in the upper Indus basin (UIB) and lower Indus basin (LIB) than in the middle Indus basin (MIB), suggesting that climate plays an important role in drought development and propagation. Results also showed a shorter and longer propagation time in the UIB and LIB, respectively. This study has helped us understand the behavior of droughts at catchment scale and will therefore help in the development of drought mitigation plans in Pakistan and similar regions around the world.
