Abstract
Before the satellite era, global sea-level reconstructions depend on tide-gauge records and in-situ hydrographic observations. However, the available global mean sea-level (GMSL) reconstructions, using different methods, indicate a spread in sea-level trend over 1900-2008 (1.3∼2.0 mm yr−1). With better understanding of the causes of sea-level change, here we implement an improved sea-level reconstruction, building upon Church and White (2011), and including three additional factors: the sea-level fingerprints, the sterodynamic sea level (SDSL) climate change patterns and more complete local vertical land motion (VLM) estimates. The trend of new GMSL reconstruction is 1.6 ± 0.2 mm yr−1 (90% confidence level) over 1900-2019, consistent with the sum of observation-based sea-level contributions of 1.5 ± 0.2 mm yr−1. The lower trend from the new reconstruction compared with the earlier Church and White (2011) result is mainly due to the updated VLM correction. The inclusion of sea-level fingerprints and SDSL climate change patterns are the dominant contributors for the improved skill of regional reconstruction. Despite GMSL budget closure in terms of long-term trend since 1900, our study shows discrepancies between the trends from available GMSL reconstructions and the sum of independent observation-based contributions over different periods in the 20th century, e.g., the discrepancy at the beginning of the 20th century, which could be related to possible bias in the land ice component estimate. The reconstruction methodology developed here, as tested with synthetic sea-level fields, could provide a promising way to identify potential biases in the individual sea-level components constrained by available global tide-gauge observations.
Abstract
Before the satellite era, global sea-level reconstructions depend on tide-gauge records and in-situ hydrographic observations. However, the available global mean sea-level (GMSL) reconstructions, using different methods, indicate a spread in sea-level trend over 1900-2008 (1.3∼2.0 mm yr−1). With better understanding of the causes of sea-level change, here we implement an improved sea-level reconstruction, building upon Church and White (2011), and including three additional factors: the sea-level fingerprints, the sterodynamic sea level (SDSL) climate change patterns and more complete local vertical land motion (VLM) estimates. The trend of new GMSL reconstruction is 1.6 ± 0.2 mm yr−1 (90% confidence level) over 1900-2019, consistent with the sum of observation-based sea-level contributions of 1.5 ± 0.2 mm yr−1. The lower trend from the new reconstruction compared with the earlier Church and White (2011) result is mainly due to the updated VLM correction. The inclusion of sea-level fingerprints and SDSL climate change patterns are the dominant contributors for the improved skill of regional reconstruction. Despite GMSL budget closure in terms of long-term trend since 1900, our study shows discrepancies between the trends from available GMSL reconstructions and the sum of independent observation-based contributions over different periods in the 20th century, e.g., the discrepancy at the beginning of the 20th century, which could be related to possible bias in the land ice component estimate. The reconstruction methodology developed here, as tested with synthetic sea-level fields, could provide a promising way to identify potential biases in the individual sea-level components constrained by available global tide-gauge observations.
Abstract
The implications of definitions of excessive rainfall observations on machine learning-model forecast skill is assessed using the Colorado State University Machine Learning Probabilities (CSU-MLP) forecast system. The CSU-MLP uses historical observations along with reforecasts from a global ensemble to train random forests to probabilistically predict excessive rainfall events. Here, random forest models are trained using two distinct rainfall datasets, one that is composed of fixed-frequency (FF) average recurrence intervals exceedances and flash flood reports, and the other a compilation of flooding and rainfall proxies (Unified Flood Verification System; UFVS). Both models generate 1–3 day forecasts and are evaluated against a climatological baseline to characterize their overall skill as a function of lead time, season, and region. Model comparisons suggest that regional frequencies in excessive rainfall observations contribute to when and where the ML models issue forecasts, and subsequently their skill and reliability. Additionally, the spatio-temporal distribution of observations have implications for ML model training requirements, notably, how long of an observational record is needed to obtain skillful forecasts. Experiments reveal that shorter-trained UFVS-based models can be as skillful as longer-trained FF-based models. In essence, the UFVS dataset exhibits a more robust characterization of excessive rainfall and impacts, and machine learning models trained on more representative datasets of meteorological hazards may not require as extensive training to generate skillful forecasts.
Abstract
The implications of definitions of excessive rainfall observations on machine learning-model forecast skill is assessed using the Colorado State University Machine Learning Probabilities (CSU-MLP) forecast system. The CSU-MLP uses historical observations along with reforecasts from a global ensemble to train random forests to probabilistically predict excessive rainfall events. Here, random forest models are trained using two distinct rainfall datasets, one that is composed of fixed-frequency (FF) average recurrence intervals exceedances and flash flood reports, and the other a compilation of flooding and rainfall proxies (Unified Flood Verification System; UFVS). Both models generate 1–3 day forecasts and are evaluated against a climatological baseline to characterize their overall skill as a function of lead time, season, and region. Model comparisons suggest that regional frequencies in excessive rainfall observations contribute to when and where the ML models issue forecasts, and subsequently their skill and reliability. Additionally, the spatio-temporal distribution of observations have implications for ML model training requirements, notably, how long of an observational record is needed to obtain skillful forecasts. Experiments reveal that shorter-trained UFVS-based models can be as skillful as longer-trained FF-based models. In essence, the UFVS dataset exhibits a more robust characterization of excessive rainfall and impacts, and machine learning models trained on more representative datasets of meteorological hazards may not require as extensive training to generate skillful forecasts.
Abstract
Probabilistic forecasts derived from ensemble prediction systems (EPS) have become the standard basis for many products and services produced by modern operational forecasting centres. However statistical post-processing is generally required to ensure forecasts have the desired properties expected for probability-based outputs. Precipitation, a core component of any operational forecast, is particularly challenging to calibrate due to its discontinuous nature and the extreme skew in rainfall amounts. A skillful forecasting system must maintain accuracy for low-to-moderate precipitation amounts, but preserve resolvability for high-to-extreme rainfall amounts, which, though rare, are important to forecast accurately in the interest of public safety. Existing statistical and machine-learning approaches to rainfall calibration address this problem, but each has drawbacks in design, training approaches, and/or performance. We describe RainForests, a machine-learning approach for calibrating ensemble rainfall forecasts using gradient-boosted decision trees. The model is based on the ecPoint system recently developed at ECMWF by Hewson and Pillosu (2021), but uses machine-learning models in place of the semi-subjective decision trees of ecPoint, along with some other improvements to the model structure. We evaluate RainForests on the Australian domain against some simple benchmarks, and show that it outperforms standard calibration approaches both in overall skill and in accurately forecasting high rainfall conditions, while being computationally efficient enough to be used in an operational forecasting system.
Abstract
Probabilistic forecasts derived from ensemble prediction systems (EPS) have become the standard basis for many products and services produced by modern operational forecasting centres. However statistical post-processing is generally required to ensure forecasts have the desired properties expected for probability-based outputs. Precipitation, a core component of any operational forecast, is particularly challenging to calibrate due to its discontinuous nature and the extreme skew in rainfall amounts. A skillful forecasting system must maintain accuracy for low-to-moderate precipitation amounts, but preserve resolvability for high-to-extreme rainfall amounts, which, though rare, are important to forecast accurately in the interest of public safety. Existing statistical and machine-learning approaches to rainfall calibration address this problem, but each has drawbacks in design, training approaches, and/or performance. We describe RainForests, a machine-learning approach for calibrating ensemble rainfall forecasts using gradient-boosted decision trees. The model is based on the ecPoint system recently developed at ECMWF by Hewson and Pillosu (2021), but uses machine-learning models in place of the semi-subjective decision trees of ecPoint, along with some other improvements to the model structure. We evaluate RainForests on the Australian domain against some simple benchmarks, and show that it outperforms standard calibration approaches both in overall skill and in accurately forecasting high rainfall conditions, while being computationally efficient enough to be used in an operational forecasting system.
Abstract
Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting WRF model, namely WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the MPI-ESM Representative Concentration Pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980-1989) and future (2040-2049) times. In the warmer (+1.9 °C for temperature and +2% for evaporation) and drier (−21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 hours (from 14.3 hours) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 hours (from 12.9 hours) partly due to slower fallout of precipitating moisture components.
Abstract
Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting WRF model, namely WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the MPI-ESM Representative Concentration Pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980-1989) and future (2040-2049) times. In the warmer (+1.9 °C for temperature and +2% for evaporation) and drier (−21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 hours (from 14.3 hours) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 hours (from 12.9 hours) partly due to slower fallout of precipitating moisture components.
Abstract
In this study, we introduce an ensemble approach to provide a probabilistic seasonal outlook of the length and seasonal rainfall anomaly of the wet season over Florida using the observed variations of the onset date of the season at the granularity of ∼10km grid resolution (which is the spatial resolution of the observed rainfall data used for this work). The timeseries of daily precipitation at the grid resolution of NASA’s Global Precipitation Mission is randomly perturbed 1000 times to account for the uncertainty of synoptic to mesoscale variations on the diagnosis of the onset and demise date of the wet season. The strong co-variability of the onset date with the seasonal length and seasonal rainfall anomaly of the wet season is then leveraged to provide the seasonal outlooks by monitoring the onset date of the wet season. This simple seasonal outlook is effective in predicting extreme tercile and even extreme pentile anomalies across Florida. We suggest that the proposed approach to the seasonal outlook of the wet season of Florida provides a viable alternative in the absence of strong external forcing like ENSO or tropical Atlantic variability that potentially limits the predictability of numerical climate models used for seasonal prediction.
Abstract
In this study, we introduce an ensemble approach to provide a probabilistic seasonal outlook of the length and seasonal rainfall anomaly of the wet season over Florida using the observed variations of the onset date of the season at the granularity of ∼10km grid resolution (which is the spatial resolution of the observed rainfall data used for this work). The timeseries of daily precipitation at the grid resolution of NASA’s Global Precipitation Mission is randomly perturbed 1000 times to account for the uncertainty of synoptic to mesoscale variations on the diagnosis of the onset and demise date of the wet season. The strong co-variability of the onset date with the seasonal length and seasonal rainfall anomaly of the wet season is then leveraged to provide the seasonal outlooks by monitoring the onset date of the wet season. This simple seasonal outlook is effective in predicting extreme tercile and even extreme pentile anomalies across Florida. We suggest that the proposed approach to the seasonal outlook of the wet season of Florida provides a viable alternative in the absence of strong external forcing like ENSO or tropical Atlantic variability that potentially limits the predictability of numerical climate models used for seasonal prediction.
Abstract
The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades’ worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm’s efficacy in improving hailstorm detection resolution.
Significance Statement
In recent years, China’s hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management.
Abstract
The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades’ worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm’s efficacy in improving hailstorm detection resolution.
Significance Statement
In recent years, China’s hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management.
Abstract
The forest microclimate shapes many aspects of forest functioning, including plant regeneration and wildfire occurrence. In complex landscapes with varying terrain, the forest microclimate varies at fine spatial scales (10–100 m2). However, accurately mapping this variation remains challenging. Vapor pressure deficit (VPD) is an important microclimatic variable for plant growth and fire activity, yet few studies have specifically focused on downscaling VPD. The aim of this study was to examine the drivers of in-forest VPD in temperate eucalypt forests and develop a model to predict in-forest VPD. We use microclimate data from 37 in-forest weather stations, installed across an aridity gradient in southeastern Australia. We used linear mixed models within an information theoretic approach to develop a predictive model for daily maximum in-forest VPD using open VPD and topographic variables. The highest-ranked model included fundamental topographic drivers of vegetation structure and microclimate in forested landscapes: aspect, elevation, and slope, in addition to open VPD. The model had high accuracy when tested against independent data. We used this model to map fine-scale (30 m2, daily) maximum in-forest VPD across a topographically complex case study landscape. Predicted in-forest VPD showed considerable spatial and temporal variations not captured by coarse-scale open VPD. This represents a significant advancement in our ability to model microclimatic conditions in temperate eucalypt forests and has the potential to advance our understanding of how ecosystem processes vary at fine spatial scales.
Abstract
The forest microclimate shapes many aspects of forest functioning, including plant regeneration and wildfire occurrence. In complex landscapes with varying terrain, the forest microclimate varies at fine spatial scales (10–100 m2). However, accurately mapping this variation remains challenging. Vapor pressure deficit (VPD) is an important microclimatic variable for plant growth and fire activity, yet few studies have specifically focused on downscaling VPD. The aim of this study was to examine the drivers of in-forest VPD in temperate eucalypt forests and develop a model to predict in-forest VPD. We use microclimate data from 37 in-forest weather stations, installed across an aridity gradient in southeastern Australia. We used linear mixed models within an information theoretic approach to develop a predictive model for daily maximum in-forest VPD using open VPD and topographic variables. The highest-ranked model included fundamental topographic drivers of vegetation structure and microclimate in forested landscapes: aspect, elevation, and slope, in addition to open VPD. The model had high accuracy when tested against independent data. We used this model to map fine-scale (30 m2, daily) maximum in-forest VPD across a topographically complex case study landscape. Predicted in-forest VPD showed considerable spatial and temporal variations not captured by coarse-scale open VPD. This represents a significant advancement in our ability to model microclimatic conditions in temperate eucalypt forests and has the potential to advance our understanding of how ecosystem processes vary at fine spatial scales.
