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Abstract
We assess the sensitivity of the Weather Research and Forecasting (WRF) Model to the use of different planetary boundary layer (PBL) parameterizations focusing on air temperature and extreme heat conditions. This work aims to evaluate the performance of the WRF Model in simulating temperatures across the Middle East–North Africa (MENA) domain, explain the model biases resulting from the choice of different PBL schemes, and identify the best-performing configuration for the MENA region. Three different PBL schemes are used to downscale the ECMWF ERA-Interim climate over the MENA region at a horizontal resolution of 24 km, for the period 2000–10. These are the Mellor–Yamada–Janjić (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2). For the evaluation of the WRF runs, we used related meteorological variables from the ERA5 reanalysis, including summer maximum and minimum 2-m air temperature and heat extreme indices. Our results indicate that simulations tend to overestimate maximum temperatures and underestimate minimum temperatures, and we find that model errors are very dependent on the geographic location. The possible physical causes of model biases are investigated through the analysis of additional variables (such as boundary layer height, moisture, and heat fluxes). It is shown that differences among the PBL schemes are associated with differences in vertical mixing strength, which alters the magnitude of the entrainment of free-tropospheric air into the PBL. The YSU is found to be the best-performing scheme, and it is recommended in WRF climate simulations for the MENA region.
Abstract
We assess the sensitivity of the Weather Research and Forecasting (WRF) Model to the use of different planetary boundary layer (PBL) parameterizations focusing on air temperature and extreme heat conditions. This work aims to evaluate the performance of the WRF Model in simulating temperatures across the Middle East–North Africa (MENA) domain, explain the model biases resulting from the choice of different PBL schemes, and identify the best-performing configuration for the MENA region. Three different PBL schemes are used to downscale the ECMWF ERA-Interim climate over the MENA region at a horizontal resolution of 24 km, for the period 2000–10. These are the Mellor–Yamada–Janjić (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2). For the evaluation of the WRF runs, we used related meteorological variables from the ERA5 reanalysis, including summer maximum and minimum 2-m air temperature and heat extreme indices. Our results indicate that simulations tend to overestimate maximum temperatures and underestimate minimum temperatures, and we find that model errors are very dependent on the geographic location. The possible physical causes of model biases are investigated through the analysis of additional variables (such as boundary layer height, moisture, and heat fluxes). It is shown that differences among the PBL schemes are associated with differences in vertical mixing strength, which alters the magnitude of the entrainment of free-tropospheric air into the PBL. The YSU is found to be the best-performing scheme, and it is recommended in WRF climate simulations for the MENA region.
Abstract
We have developed additive logistic models for the occurrence of lightning, large (≥ 2 cm), and very large (≥ 5 cm) hail to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients-based approach. The resulting hail models outperform the Significant Hail Parameter and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the -10°C isotherm was found to be a more universally skilful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modelled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian Plains. In the southern Plains, a period of enhanced storm activity occurred in the 1980s and 1990s.
Abstract
We have developed additive logistic models for the occurrence of lightning, large (≥ 2 cm), and very large (≥ 5 cm) hail to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients-based approach. The resulting hail models outperform the Significant Hail Parameter and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the -10°C isotherm was found to be a more universally skilful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modelled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian Plains. In the southern Plains, a period of enhanced storm activity occurred in the 1980s and 1990s.
Abstract
North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful.
Significance Statement
The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.
Abstract
North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful.
Significance Statement
The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.
Abstract
Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather stations (PWS) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models’ evaluation, but can also serve for bias-correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research Forecast (WRF) model and its Building Effect Parameterization with the Building Energy Model (BEP-BEM) activated, we evaluated the modelled temperatures against 402 urban PWS and showcased a heterogeneous spatial distribution of the model’s cool bias that was not captured using of with the Building Energy Modelficial weather stations only. This finding indicated a need for spatially-explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models’ biases in each urban grid cell. This bias correction technique is the first to consider that modelled urban temperatures follow a non-linear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias-correction was beneficial to bias-correct daily-minimum, -mean, and -maximum temperatures in the cities. We recommend that urban climate modellers further investigate the use of quality-checked PWS for model evaluation and derive a framework for bias-correction of urban climate simulations that can serve urban climate impact studies.
Abstract
Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather stations (PWS) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models’ evaluation, but can also serve for bias-correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research Forecast (WRF) model and its Building Effect Parameterization with the Building Energy Model (BEP-BEM) activated, we evaluated the modelled temperatures against 402 urban PWS and showcased a heterogeneous spatial distribution of the model’s cool bias that was not captured using of with the Building Energy Modelficial weather stations only. This finding indicated a need for spatially-explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models’ biases in each urban grid cell. This bias correction technique is the first to consider that modelled urban temperatures follow a non-linear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias-correction was beneficial to bias-correct daily-minimum, -mean, and -maximum temperatures in the cities. We recommend that urban climate modellers further investigate the use of quality-checked PWS for model evaluation and derive a framework for bias-correction of urban climate simulations that can serve urban climate impact studies.
Abstract
To reduce the amount of nonclimatic biases of air temperature in each weather station’s record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases.” This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ∼60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ∼20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed.
Significance Statement
Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.
Abstract
To reduce the amount of nonclimatic biases of air temperature in each weather station’s record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases.” This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ∼60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ∼20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed.
Significance Statement
Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.
Abstract
Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (>70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT < 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties.
Significance Statement
We aim to identify the properties of tropical convective clouds (TCCs) that have a high precipitation probability. We designed a two-step framework to identify TCC objects and the conditions of cloud properties for TCCs to have a high precipitation probability. The TCCs with a precipitation probability > 70% tend to have a low cloud-top temperature and a cloud particle effective radius ≥ 20 μm. Cloud optical thicknesses are distributed over a wide range, but thinning requires a particle radius larger than 20 μm. These conditions of cloud properties appear to be unchanged under various spatial–temporal conditions over the tropics. This important observational finding advances our understanding of the cloud–precipitation relationship in TCCs and can be applied to satellite nowcasting of precipitation in the tropics, where numerical weather forecasts are limited.
Abstract
Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (>70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT < 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties.
Significance Statement
We aim to identify the properties of tropical convective clouds (TCCs) that have a high precipitation probability. We designed a two-step framework to identify TCC objects and the conditions of cloud properties for TCCs to have a high precipitation probability. The TCCs with a precipitation probability > 70% tend to have a low cloud-top temperature and a cloud particle effective radius ≥ 20 μm. Cloud optical thicknesses are distributed over a wide range, but thinning requires a particle radius larger than 20 μm. These conditions of cloud properties appear to be unchanged under various spatial–temporal conditions over the tropics. This important observational finding advances our understanding of the cloud–precipitation relationship in TCCs and can be applied to satellite nowcasting of precipitation in the tropics, where numerical weather forecasts are limited.
Abstract
In a future world where most of the energy must come from intermittent renewable energy sources such as wind or solar energy, it would be more efficient if, for each demand area, we could determine the locations for which the output of an energy source would naturally match the demand fluctuations from that area. In parallel, meteorological weather systems such as midlatitude cyclones are often organized in a way that naturally shapes where areas of greater energy need (e.g., regions with more cold air) are with respect to windier or sunnier areas, and these are generally not collocated. As a result, the best places to generate renewable energy may not be near consumption sites; these may be determined, however, by common meteorological patterns. Using data from a reanalysis of six decades of past weather, we determined the complementarity between different sources of energy as well as the relationships between renewable supply and demand at daily averaged time scales for several North American cities. In general, demand and solar power tend to be slightly positively correlated at nearby locations away from the Rocky Mountains; however, wind power often must be obtained from greater distances and at altitude for energy production to be better timed with consumption.
Significance Statement
Weather patterns such as high and low pressure systems shape where and when energy is needed for warming or cooling; they also shape how much renewable energy from winds and the sun can be produced. Hence, they determine the regions where more energy is likely to be available in periods of unusually high need for each demand location. Finding where those areas are may result in more timely renewable energy production in the future to help reduce fossil fuel use for energy production.
Abstract
In a future world where most of the energy must come from intermittent renewable energy sources such as wind or solar energy, it would be more efficient if, for each demand area, we could determine the locations for which the output of an energy source would naturally match the demand fluctuations from that area. In parallel, meteorological weather systems such as midlatitude cyclones are often organized in a way that naturally shapes where areas of greater energy need (e.g., regions with more cold air) are with respect to windier or sunnier areas, and these are generally not collocated. As a result, the best places to generate renewable energy may not be near consumption sites; these may be determined, however, by common meteorological patterns. Using data from a reanalysis of six decades of past weather, we determined the complementarity between different sources of energy as well as the relationships between renewable supply and demand at daily averaged time scales for several North American cities. In general, demand and solar power tend to be slightly positively correlated at nearby locations away from the Rocky Mountains; however, wind power often must be obtained from greater distances and at altitude for energy production to be better timed with consumption.
Significance Statement
Weather patterns such as high and low pressure systems shape where and when energy is needed for warming or cooling; they also shape how much renewable energy from winds and the sun can be produced. Hence, they determine the regions where more energy is likely to be available in periods of unusually high need for each demand location. Finding where those areas are may result in more timely renewable energy production in the future to help reduce fossil fuel use for energy production.
Abstract
This paper presents work using a machine learning model to diagnose Antarctic blowing snow (BLSN) properties with the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), data. We adopt the random forest classifier for BLSN identification and the random forest regressor for BLSN optical depth and height diagnosis. BLSN properties observed from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used as the truth for training the model. Using MERRA-2 fields such as snow age, surface elevation and pressure, temperature, specific humidity, and temperature gradient at the 2-m level, and wind speed at the 10-m level as input, reasonable results are achieved. Hourly blowing snow property diagnostics are generated with the trained model. Using 2010 as an example, it is shown that the Antarctic BLSN frequency is much higher over East than West Antarctica. High-frequency months are from April to September, during which BLSN frequency exceeds 20% over East Antarctica. For May 2010, the BLSN snow frequency in the region is as high as 37%. Due to the suppression by strong surface-based inversions, larger values of BLSN height and optical depth are usually limited to the coastal regions, wherein the strength of surface-based inversions is weaker.
Abstract
This paper presents work using a machine learning model to diagnose Antarctic blowing snow (BLSN) properties with the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), data. We adopt the random forest classifier for BLSN identification and the random forest regressor for BLSN optical depth and height diagnosis. BLSN properties observed from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used as the truth for training the model. Using MERRA-2 fields such as snow age, surface elevation and pressure, temperature, specific humidity, and temperature gradient at the 2-m level, and wind speed at the 10-m level as input, reasonable results are achieved. Hourly blowing snow property diagnostics are generated with the trained model. Using 2010 as an example, it is shown that the Antarctic BLSN frequency is much higher over East than West Antarctica. High-frequency months are from April to September, during which BLSN frequency exceeds 20% over East Antarctica. For May 2010, the BLSN snow frequency in the region is as high as 37%. Due to the suppression by strong surface-based inversions, larger values of BLSN height and optical depth are usually limited to the coastal regions, wherein the strength of surface-based inversions is weaker.
