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Abstract
Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.
Significance Statement
The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.
Abstract
Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.
Significance Statement
The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.
Abstract
High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG.
Abstract
High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG.
Abstract
The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first-order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL–mixed-layer height compensation in our idealized modeling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed-layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns.
Significance Statement
Clouds and vegetation are both important components of the climate system that interact across a range of scales. These interactions are central to understanding how changes at the land surface feedback on climate. For example, if a forest expands or recedes, diagnosing how that will impact clouds will determine whether you predict warming or cooling temperatures from that shift in the forest area. These predictions are often made with complex Earth system models, but we look to a more idealized representation of the land–atmosphere system to diagnose how shallow clouds should respond to changes in surface properties with different scenarios of boreal forest expansion at a more foundational level. This both grounds our understanding of previous analysis and provides helpful direction for future studies of this relevant and impactful land cover change.
Abstract
The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first-order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL–mixed-layer height compensation in our idealized modeling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed-layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns.
Significance Statement
Clouds and vegetation are both important components of the climate system that interact across a range of scales. These interactions are central to understanding how changes at the land surface feedback on climate. For example, if a forest expands or recedes, diagnosing how that will impact clouds will determine whether you predict warming or cooling temperatures from that shift in the forest area. These predictions are often made with complex Earth system models, but we look to a more idealized representation of the land–atmosphere system to diagnose how shallow clouds should respond to changes in surface properties with different scenarios of boreal forest expansion at a more foundational level. This both grounds our understanding of previous analysis and provides helpful direction for future studies of this relevant and impactful land cover change.
Abstract
Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack also impact seasonal runoff. The roles of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a random forest–based statistical forecasting framework. We find that on average, antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, using precipitation and temperature data after the time of peak snowpack improves water supply forecasts significantly. The 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years.
Significance Statement
Seasonal water supply forecasts in western Colorado are highly valuable because spring and summer runoff from this region helps support the water supply of 40 million people. Accurate forecasts improve the management of the region’s water. Heavy investments have been made in improving our ability to monitor antecedent hydrological conditions in western Colorado, such as root zone soil moisture and groundwater. However, results from this study indicate that the largest source of uncertainty in western Colorado runoff forecasts is future weather. Therefore, improved subseasonal-to-seasonal weather forecasts for western Colorado are what is most needed to improve regional water supply forecasts, and the ability to properly manage western Colorado water.
Abstract
Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack also impact seasonal runoff. The roles of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a random forest–based statistical forecasting framework. We find that on average, antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, using precipitation and temperature data after the time of peak snowpack improves water supply forecasts significantly. The 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years.
Significance Statement
Seasonal water supply forecasts in western Colorado are highly valuable because spring and summer runoff from this region helps support the water supply of 40 million people. Accurate forecasts improve the management of the region’s water. Heavy investments have been made in improving our ability to monitor antecedent hydrological conditions in western Colorado, such as root zone soil moisture and groundwater. However, results from this study indicate that the largest source of uncertainty in western Colorado runoff forecasts is future weather. Therefore, improved subseasonal-to-seasonal weather forecasts for western Colorado are what is most needed to improve regional water supply forecasts, and the ability to properly manage western Colorado water.
Abstract
Satellites provide a useful way of estimating rainfall where the availability of in situ data is low but their indirect nature of estimation means there can be substantial biases. Consequently, the assimilation of in situ data is an important step in improving the accuracy of the satellite rainfall analysis. The effectiveness of this step varies with gauge density, and this study investigated the effectiveness of statistical interpolation (SI), also known as optimal interpolation (OI), on a monthly time scale when gauge density is extremely low using Papua New Guinea (PNG) as a study region. The topography of the region presented an additional challenge to the algorithm. An open-source implementation of SI was developed on Python 3 and confirmed to be consistent with an existing implementation, addressing a lack of open-source implementation for this classical algorithm. The effectiveness of the analysis produced by this algorithm was then compared to the pure satellite analysis over PNG from 2001 to 2014. When performance over the entire study domain was considered, the improvement from using SI was close to imperceptible because of the small number of stations available for assimilation and the small radius of influence of each station (imposed by the topography present in the domain). However, there was still value in using OI as performance around each of the stations was noticeably improved, with the error consistently being reduced along with a general increase in the correlation metric. Furthermore, in an operational context, the use of OI provides an important function of ensuring consistency between in situ data and the gridded analysis.
Significance Statement
The blending of satellite and gauge rainfall data through a process known as statistical interpolation (SI) is known to be capable of producing a more accurate dataset that facilitates better estimation of rainfall. However, the performance of this algorithm over a domain such as Papua New Guinea, where gauge density is extremely low, is not often explored. This study reveals that, although an improvement over the entire Papua New Guinea domain was slight, the algorithm is still valuable as there was a consistent improvement around the stations. Additionally, an adaptable and open-source version of the algorithm is provided, allowing users to blend their own satellite and gauge data and create better geospatial datasets for their own purposes.
Abstract
Satellites provide a useful way of estimating rainfall where the availability of in situ data is low but their indirect nature of estimation means there can be substantial biases. Consequently, the assimilation of in situ data is an important step in improving the accuracy of the satellite rainfall analysis. The effectiveness of this step varies with gauge density, and this study investigated the effectiveness of statistical interpolation (SI), also known as optimal interpolation (OI), on a monthly time scale when gauge density is extremely low using Papua New Guinea (PNG) as a study region. The topography of the region presented an additional challenge to the algorithm. An open-source implementation of SI was developed on Python 3 and confirmed to be consistent with an existing implementation, addressing a lack of open-source implementation for this classical algorithm. The effectiveness of the analysis produced by this algorithm was then compared to the pure satellite analysis over PNG from 2001 to 2014. When performance over the entire study domain was considered, the improvement from using SI was close to imperceptible because of the small number of stations available for assimilation and the small radius of influence of each station (imposed by the topography present in the domain). However, there was still value in using OI as performance around each of the stations was noticeably improved, with the error consistently being reduced along with a general increase in the correlation metric. Furthermore, in an operational context, the use of OI provides an important function of ensuring consistency between in situ data and the gridded analysis.
Significance Statement
The blending of satellite and gauge rainfall data through a process known as statistical interpolation (SI) is known to be capable of producing a more accurate dataset that facilitates better estimation of rainfall. However, the performance of this algorithm over a domain such as Papua New Guinea, where gauge density is extremely low, is not often explored. This study reveals that, although an improvement over the entire Papua New Guinea domain was slight, the algorithm is still valuable as there was a consistent improvement around the stations. Additionally, an adaptable and open-source version of the algorithm is provided, allowing users to blend their own satellite and gauge data and create better geospatial datasets for their own purposes.
Abstract
The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.
Abstract
The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.
Abstract
The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple U.K. soil moisture products, including satellite-merged (i.e., TCM), in situ (i.e., COSMOS-UK), hydrological model [i.e., Grid-to-Grid (G2G)], statistical model [i.e., Soil Moisture U.K. (SMUK)], and land surface model (LSM) [i.e., Climate Hydrology and Ecology research Support System (CHESS)] data. The drydown decay time scale (τ) for all gridded products is computed at an unprecedented resolution of 1–2 km, a scale relevant to weather and climate models. While their range of τ differs (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyze the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales.
Significance Statement
While important for many aspects of the environment, the evaluation of modeled soil moisture has remained incredibly challenging. Sensors work at different space and time scales to the models, the definitions of soil moisture vary between applications, and the soil moisture itself is subject to the soil properties while the impact of the soil moisture on evaporation or river flow is more dependent on its variation in time and space than its absolute value. What we need is a method that allows us to compare the important features of soil moisture rather than its value. In this study, we choose to study drydown as a way to capture and compare the behavior of different soil moisture data products.
Abstract
The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple U.K. soil moisture products, including satellite-merged (i.e., TCM), in situ (i.e., COSMOS-UK), hydrological model [i.e., Grid-to-Grid (G2G)], statistical model [i.e., Soil Moisture U.K. (SMUK)], and land surface model (LSM) [i.e., Climate Hydrology and Ecology research Support System (CHESS)] data. The drydown decay time scale (τ) for all gridded products is computed at an unprecedented resolution of 1–2 km, a scale relevant to weather and climate models. While their range of τ differs (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyze the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales.
Significance Statement
While important for many aspects of the environment, the evaluation of modeled soil moisture has remained incredibly challenging. Sensors work at different space and time scales to the models, the definitions of soil moisture vary between applications, and the soil moisture itself is subject to the soil properties while the impact of the soil moisture on evaporation or river flow is more dependent on its variation in time and space than its absolute value. What we need is a method that allows us to compare the important features of soil moisture rather than its value. In this study, we choose to study drydown as a way to capture and compare the behavior of different soil moisture data products.
Abstract
The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes.
Significance Statement
This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.
Abstract
The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes.
Significance Statement
This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.
Abstract
Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS), to explore the connections between the skill of the TC track, intensity, precipitation and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyse the connections between each forecast component. Strong relationships are found between track, precipitation and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eye wall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases, to investigate how this cascade of predictability varies with different forecast and geographical contexts, to help inform flood early warning in TCs.
Abstract
Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS), to explore the connections between the skill of the TC track, intensity, precipitation and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyse the connections between each forecast component. Strong relationships are found between track, precipitation and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eye wall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases, to investigate how this cascade of predictability varies with different forecast and geographical contexts, to help inform flood early warning in TCs.
Abstract
Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understand how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have higher precipitation rate than the previous version, except for the radar-only product. Within ~65°S-N, covered by all of the instruments, this increase ranges from about 9% for the combined radar-radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics, (25°S-25°N) passive microwave sounders show the highest precipitation rate among all of the precipitation products studied, and the highest increase (~19%) compared to their previous version. Precipitation products are least consistent in mid-latitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.
Abstract
Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understand how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have higher precipitation rate than the previous version, except for the radar-only product. Within ~65°S-N, covered by all of the instruments, this increase ranges from about 9% for the combined radar-radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics, (25°S-25°N) passive microwave sounders show the highest precipitation rate among all of the precipitation products studied, and the highest increase (~19%) compared to their previous version. Precipitation products are least consistent in mid-latitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.
