Abstract
Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.
Abstract
Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.
Abstract
The weather and climate greatly affect the socioeconomic activities on multiple temporal and spatial scales. From a climate perspective, atmospheric and ocean characteristics have determined the life, evolution and prosperity of humans and other species in different areas of the world. On smaller scales, the atmospheric and sea conditions affect various sectors such as civil protection, food security, communications, transportation and insurance. It becomes evident that weather and ocean forecasting is high value information highlighting the need for state-of-the-art forecasting systems to be adopted. This importance has been acknowledged by the authorities of Saudi Arabia entrusting the National Center for Meteorology (NCM) to provide high quality weather and climate analytics. This led to the development of a numerical weather prediction (NWP) system. The new system includes weather, wave and ocean circulation components and has been operational since 2020 enhancing the national capabilities in NWP. Within this article, a description of the system and its performance is discussed alongside future goals.
Abstract
The weather and climate greatly affect the socioeconomic activities on multiple temporal and spatial scales. From a climate perspective, atmospheric and ocean characteristics have determined the life, evolution and prosperity of humans and other species in different areas of the world. On smaller scales, the atmospheric and sea conditions affect various sectors such as civil protection, food security, communications, transportation and insurance. It becomes evident that weather and ocean forecasting is high value information highlighting the need for state-of-the-art forecasting systems to be adopted. This importance has been acknowledged by the authorities of Saudi Arabia entrusting the National Center for Meteorology (NCM) to provide high quality weather and climate analytics. This led to the development of a numerical weather prediction (NWP) system. The new system includes weather, wave and ocean circulation components and has been operational since 2020 enhancing the national capabilities in NWP. Within this article, a description of the system and its performance is discussed alongside future goals.
Abstract
Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n = 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions.
Significance Statement
Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.
Abstract
Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n = 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions.
Significance Statement
Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.
Abstract
North Atlantic atmosphere–ocean variability is assessed in climate model simulations from HighResMIP that have low-resolution (LR) or high-resolution (HR) in their atmosphere and ocean model components. It is found that some of the LR simulations overestimate the low-frequency variability of subpolar sea surface temperature (SST) anomalies and underestimate its correlation with the NAO compared to ERA5 reanalysis. These deficiencies are significantly reduced in the HR simulations, and it is shown that the improvements are related to a reduction of intrinsic (non-NAO-driven) variability of the subpolar ocean circulation. To understand the cause of the overestimated intrinsic subpolar ocean variability in the LR simulations, a link is demonstrated between the amplitude of the subpolar ocean variability and the mean state of the Labrador–Irminger seas. Supporting previous studies, the Labrador–Irminger seas tend to be colder and fresher in the LR simulations compared to the HR simulations and oceanic observations from EN4. This promotes upper-ocean density anomalies in this region to be more salinity-controlled in the LR simulations versus more temperature-controlled in the HR simulations and EN4 observations. It is argued that this causes the excessive subpolar ocean variability in the LR simulations by favoring a positive feedback between subpolar upper-ocean salinity and Atlantic Meridional Overturning Circulation (AMOC) anomalies, rather than a negative feedback between subpolar SST and AMOC anomalies as in the HR simulations. The findings overall suggest that the subpolar ocean mean state impacts the variability of the ocean circulation and SSTs, including their relationship with the atmospheric circulation, in the extratropical North Atlantic.
Abstract
North Atlantic atmosphere–ocean variability is assessed in climate model simulations from HighResMIP that have low-resolution (LR) or high-resolution (HR) in their atmosphere and ocean model components. It is found that some of the LR simulations overestimate the low-frequency variability of subpolar sea surface temperature (SST) anomalies and underestimate its correlation with the NAO compared to ERA5 reanalysis. These deficiencies are significantly reduced in the HR simulations, and it is shown that the improvements are related to a reduction of intrinsic (non-NAO-driven) variability of the subpolar ocean circulation. To understand the cause of the overestimated intrinsic subpolar ocean variability in the LR simulations, a link is demonstrated between the amplitude of the subpolar ocean variability and the mean state of the Labrador–Irminger seas. Supporting previous studies, the Labrador–Irminger seas tend to be colder and fresher in the LR simulations compared to the HR simulations and oceanic observations from EN4. This promotes upper-ocean density anomalies in this region to be more salinity-controlled in the LR simulations versus more temperature-controlled in the HR simulations and EN4 observations. It is argued that this causes the excessive subpolar ocean variability in the LR simulations by favoring a positive feedback between subpolar upper-ocean salinity and Atlantic Meridional Overturning Circulation (AMOC) anomalies, rather than a negative feedback between subpolar SST and AMOC anomalies as in the HR simulations. The findings overall suggest that the subpolar ocean mean state impacts the variability of the ocean circulation and SSTs, including their relationship with the atmospheric circulation, in the extratropical North Atlantic.
Abstract
Salt mixing enables the transport of water between the inflow and outflow layers of estuarine circulation and therefore closes the circulation by driving a diahaline exchange flow. A recently derived universal law links the salt mixing inside an estuarine volume bounded by an isohaline surface to freshwater discharge: it states that on long-term average, the area-integrated mixing across the bounding isohaline is directly proportional to the freshwater discharge entering the estuary. However, even though numerous studies predict that periods of extreme discharge will become more frequent with climate change, the direct impact of such periods on estuarine mixing and circulation has yet to be investigated. Therefore, this numerical modeling study focuses on salinity mixing and diahaline exchange flows during a low-discharge and an extreme high-discharge period. To this end, we apply a realistic numerical setup of the Elbe estuary in northern Germany, using curvilinear coordinates that follow the navigational channel. This is the first time the direct relationship between diahaline exchange flow and salt mixing as well as the spatial distribution of the diahaline exchange flow are shown in a realistic tidal setup. The spatial distribution is highly correlated with the local mixing gradient for salinity, such that inflow occurs near the bottom at the upstream end of the isohaline. Meanwhile, outflow occurs near the surface at its downstream end. Lastly, increased vertical stratification occurs within the estuary during the high-discharge period, while estuarine-wide mixing strongly converges to the universal law for averaging periods of the discharge event time scale.
Abstract
Salt mixing enables the transport of water between the inflow and outflow layers of estuarine circulation and therefore closes the circulation by driving a diahaline exchange flow. A recently derived universal law links the salt mixing inside an estuarine volume bounded by an isohaline surface to freshwater discharge: it states that on long-term average, the area-integrated mixing across the bounding isohaline is directly proportional to the freshwater discharge entering the estuary. However, even though numerous studies predict that periods of extreme discharge will become more frequent with climate change, the direct impact of such periods on estuarine mixing and circulation has yet to be investigated. Therefore, this numerical modeling study focuses on salinity mixing and diahaline exchange flows during a low-discharge and an extreme high-discharge period. To this end, we apply a realistic numerical setup of the Elbe estuary in northern Germany, using curvilinear coordinates that follow the navigational channel. This is the first time the direct relationship between diahaline exchange flow and salt mixing as well as the spatial distribution of the diahaline exchange flow are shown in a realistic tidal setup. The spatial distribution is highly correlated with the local mixing gradient for salinity, such that inflow occurs near the bottom at the upstream end of the isohaline. Meanwhile, outflow occurs near the surface at its downstream end. Lastly, increased vertical stratification occurs within the estuary during the high-discharge period, while estuarine-wide mixing strongly converges to the universal law for averaging periods of the discharge event time scale.
Abstract
Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at sub-km scale), and temporal evolution (at ~2-min resolution) of convective cells.
This adaptation of MAAS guided two mechanically scanning C-band radars (the CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect 3 sector Plan Position Indicator (PPI) scans towards the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of 3-6 Range Height Indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a pre-determined set of criteria.
Between 01 June and 30 September 2022 over 315,000 vertical cross-section observations were collected by the C-band radars through ~1,300 unique isolated convective cells, most of which were observed for over 15-min of their lifecycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.
Abstract
Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at sub-km scale), and temporal evolution (at ~2-min resolution) of convective cells.
This adaptation of MAAS guided two mechanically scanning C-band radars (the CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect 3 sector Plan Position Indicator (PPI) scans towards the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of 3-6 Range Height Indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a pre-determined set of criteria.
Between 01 June and 30 September 2022 over 315,000 vertical cross-section observations were collected by the C-band radars through ~1,300 unique isolated convective cells, most of which were observed for over 15-min of their lifecycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.
Abstract
The Qinghai–Tibet Plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial resolution (∼30 m) with good accuracy. Multiple sensors’ observations are available, but producing reliable long time series surface water mapping at a subannual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural-network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000–20 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency > 60%) was 26.66 × 103 km2 in 2020. The overall, producer, and user accuracies of our surface water map were 0.96, 0.94, and 0.98, respectively, and the kappa coefficient reached 0.90, demonstrating a better performance than existing products [i.e., Joint Research Centre (JRC) Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89]. Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and a priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future.
Significance Statement
In this paper, we present a new methodology to estimate surface water and its intra-annual changes using Landsat data. Missing data and retrieval errors in the winter are major issues in the existing products (i.e., JRC dataset). This motivated us to develop a new machine learning algorithm to better improve the retrieval scheme. We show that our approach, based on a neural network classifier, delivers a significant improvement compared to the previous estimates. As shown in the literature, JRC data can hardly be used at the monthly level, whereas our retrieval appears to be exploitable at the monthly scale. This is essential to understand the trend in surface water, one of the key elements of the water cycle.
Abstract
The Qinghai–Tibet Plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial resolution (∼30 m) with good accuracy. Multiple sensors’ observations are available, but producing reliable long time series surface water mapping at a subannual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural-network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000–20 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency > 60%) was 26.66 × 103 km2 in 2020. The overall, producer, and user accuracies of our surface water map were 0.96, 0.94, and 0.98, respectively, and the kappa coefficient reached 0.90, demonstrating a better performance than existing products [i.e., Joint Research Centre (JRC) Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89]. Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and a priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future.
Significance Statement
In this paper, we present a new methodology to estimate surface water and its intra-annual changes using Landsat data. Missing data and retrieval errors in the winter are major issues in the existing products (i.e., JRC dataset). This motivated us to develop a new machine learning algorithm to better improve the retrieval scheme. We show that our approach, based on a neural network classifier, delivers a significant improvement compared to the previous estimates. As shown in the literature, JRC data can hardly be used at the monthly level, whereas our retrieval appears to be exploitable at the monthly scale. This is essential to understand the trend in surface water, one of the key elements of the water cycle.
Abstract
Rainfall and temperature extremes have become more frequent and severe in recent times due to changing climate. Since these catastrophic occurrences directly affect a region’s hydrology, it is imperative to develop models that can project and explain the joint behavior of climate variables. Copula functions have been used relatively successfully to capture multivariate processes. With climate being a multifaceted process, there is interdependence between variables, making copula use desirable since traditional bivariate distributions do not account for the dependent structure. In this study, we introduced a bivariate exponentiated Teissier distribution based on a Clayton copula. For parameter estimation, the maximum likelihood and inference functions for margin approaches are used. A simulation study that considered various sets of parameters is also conducted in order to select the most efficient parameter estimation method. Last, the applicability of the proposed model is demonstrated using real-world data from flood and temperature processes. After fitting, the log-likelihood, Akaike information criteria (AIC), and Bayesian information criteria (BIC) values of the proposed model are −145.00, 300.00, and 311.71 for flood data, respectively, and −128.71, 267.42, and 275.98 for temperature data, respectively. Estimated parameters are
Abstract
Rainfall and temperature extremes have become more frequent and severe in recent times due to changing climate. Since these catastrophic occurrences directly affect a region’s hydrology, it is imperative to develop models that can project and explain the joint behavior of climate variables. Copula functions have been used relatively successfully to capture multivariate processes. With climate being a multifaceted process, there is interdependence between variables, making copula use desirable since traditional bivariate distributions do not account for the dependent structure. In this study, we introduced a bivariate exponentiated Teissier distribution based on a Clayton copula. For parameter estimation, the maximum likelihood and inference functions for margin approaches are used. A simulation study that considered various sets of parameters is also conducted in order to select the most efficient parameter estimation method. Last, the applicability of the proposed model is demonstrated using real-world data from flood and temperature processes. After fitting, the log-likelihood, Akaike information criteria (AIC), and Bayesian information criteria (BIC) values of the proposed model are −145.00, 300.00, and 311.71 for flood data, respectively, and −128.71, 267.42, and 275.98 for temperature data, respectively. Estimated parameters are
