Abstract
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-year period versus nineteen high quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to timescale, meteorological season, PMW source, QI, and land surface type. Results indicate that: (1) the cold season’s ( Nov - Apr ) larger relative bias can be mitigated via backward morphing; (2) IMERG 6-hour precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index - 1 (FBI-1); (3) the performance of five PMW is affected by the season to different degrees; (4) in terms of some metrics, skills do not always enhance with increasing QI; (5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.
Abstract
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-year period versus nineteen high quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to timescale, meteorological season, PMW source, QI, and land surface type. Results indicate that: (1) the cold season’s ( Nov - Apr ) larger relative bias can be mitigated via backward morphing; (2) IMERG 6-hour precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index - 1 (FBI-1); (3) the performance of five PMW is affected by the season to different degrees; (4) in terms of some metrics, skills do not always enhance with increasing QI; (5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.
Abstract
The characteristics of in-storm cooling occurred ahead-of-eye-center are investigated based on a combination of observations and numerical simulations, as well as its sensitivity to tropical cyclone (TC) characteristics and oceanic climatological conditions. A composite of drifter and remote sensing observations from 1979 to 2020 in the North Hemisphere statistically evidences that the percentage of TC-induced ahead-of-eye-center cooling is enhanced remarkably over the coastal ocean than that over the open sea, no matter what the TC intensity, translation speed and pre-storm SST conditions are. Results are statistically similar when the actual ahead-of-eye SST cooling is used. Idealized numerical simulation results show that as the TC center approaches the coastline, the percentage of ahead-of-eye-center cooling increases steadily with the water depth shallowing below 100 meters. This phenomenon may not be caused by strong stratification of the coastal ocean, as previous studies suggested. An ocean heat balance analysis reveals a new mechanism responsible for the enhanced percentage of ahead-of-eye-center cooling near the coast: although the vertical mixing dominates in the surface cooling process over the open sea, broad and intense advection is largely responsible for the rapid increase of percentage of ahead-of-eye-center cooling over the coastal ocean, owing to less cold-water entrainment from below. A series of sensitivity experiments are conducted by varying TC characteristics in terms of intensity, translation speed, radius of maximum wind speed, and ocean characteristics in terms of temperature profiles and slope rates of the shelf. The percentage of ahead-of-eye-center cooling is dependent on the intensity and translation speed of TCs, but showing little sensitivity to other parameters.
Abstract
The characteristics of in-storm cooling occurred ahead-of-eye-center are investigated based on a combination of observations and numerical simulations, as well as its sensitivity to tropical cyclone (TC) characteristics and oceanic climatological conditions. A composite of drifter and remote sensing observations from 1979 to 2020 in the North Hemisphere statistically evidences that the percentage of TC-induced ahead-of-eye-center cooling is enhanced remarkably over the coastal ocean than that over the open sea, no matter what the TC intensity, translation speed and pre-storm SST conditions are. Results are statistically similar when the actual ahead-of-eye SST cooling is used. Idealized numerical simulation results show that as the TC center approaches the coastline, the percentage of ahead-of-eye-center cooling increases steadily with the water depth shallowing below 100 meters. This phenomenon may not be caused by strong stratification of the coastal ocean, as previous studies suggested. An ocean heat balance analysis reveals a new mechanism responsible for the enhanced percentage of ahead-of-eye-center cooling near the coast: although the vertical mixing dominates in the surface cooling process over the open sea, broad and intense advection is largely responsible for the rapid increase of percentage of ahead-of-eye-center cooling over the coastal ocean, owing to less cold-water entrainment from below. A series of sensitivity experiments are conducted by varying TC characteristics in terms of intensity, translation speed, radius of maximum wind speed, and ocean characteristics in terms of temperature profiles and slope rates of the shelf. The percentage of ahead-of-eye-center cooling is dependent on the intensity and translation speed of TCs, but showing little sensitivity to other parameters.
Abstract
The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the ‘Aerosol-Cloud’ (AC) model in: 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the UK, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF model and has hybrid bin/bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based and satellite observations for all three cases. Filtered concentrations of ice (> 0.1 to 0.2 mm) agree with those observed at all sampled levels.
AC forms ice heterogeneously through condensation, contact, deposition, and immersion freezing. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity.
In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5 to 1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80-90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett-Mossop process and fragmentation in ice-ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes < 10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity.
Abstract
The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the ‘Aerosol-Cloud’ (AC) model in: 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the UK, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF model and has hybrid bin/bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based and satellite observations for all three cases. Filtered concentrations of ice (> 0.1 to 0.2 mm) agree with those observed at all sampled levels.
AC forms ice heterogeneously through condensation, contact, deposition, and immersion freezing. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity.
In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5 to 1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80-90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett-Mossop process and fragmentation in ice-ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes < 10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity.
Abstract
Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions non-parametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 — 2020, comparing results from the PF with those from an Ensemble Kalman Filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within pre-existing data assimilation frameworks.
Abstract
Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions non-parametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 — 2020, comparing results from the PF with those from an Ensemble Kalman Filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within pre-existing data assimilation frameworks.
Current human-induced warming has led to approximately a 30-fold increase in the occurrence probability of 2021 northwestern Pacific concurrent marine and terrestrial summer heat.
Current human-induced warming has led to approximately a 30-fold increase in the occurrence probability of 2021 northwestern Pacific concurrent marine and terrestrial summer heat.
Abstract
The Met Office in the United Kingdom has developed a completely new probabilistic postprocessing system called IMPROVER to operate on outputs from its operational numerical weather prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders while better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts, and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop, and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous postprocessing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on predefined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes–no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in spring 2022.
Abstract
The Met Office in the United Kingdom has developed a completely new probabilistic postprocessing system called IMPROVER to operate on outputs from its operational numerical weather prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders while better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts, and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop, and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous postprocessing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on predefined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes–no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in spring 2022.
Abstract
A Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) follow-on constellation, COSMIC-2, was successfully launched into equatorial orbit on June 24, 2019. With an increased signal-to-noise ratio due to improved receivers and digital beam-steering antennas, COSMIC-2 is producing about 5,000 high-quality radio-occultation (RO) profiles daily over the tropics and subtropics. The initial evaluation of the impact of assimilating COSMIC-2 into NOAA’s Global Forecast System (GFS) showed mixed results, and adjustments to quality control procedures and observation error characteristics had to be made prior to the assimilation of this dataset in the operational configuration in May 2020. Additional changes in the GFS that followed this initial operational implementation resulted in a larger percentage of rejection (~ 90 %) of all RO observations, including COSMIC-2, in the mid-lower troposphere. Since then, two software upgrades directly related to the assimilation of RO bending angle observations were developed. These improvements aimed at optimizing the utilization of COSMIC-2 and other RO observations to improve global weather analyses and forecasts. The first upgrade was implemented operationally in September 2021 and the second one in November 2022. This study describes both RO software upgrades and evaluates the impact of COSMIC-2 with this most recently improved configuration. Specifically, we show that the assimilation of COSMIC-2 observations has a significant impact in improving temperature and winds in the tropics, though benefits also extend to the extra-tropical latitudes.
Abstract
A Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) follow-on constellation, COSMIC-2, was successfully launched into equatorial orbit on June 24, 2019. With an increased signal-to-noise ratio due to improved receivers and digital beam-steering antennas, COSMIC-2 is producing about 5,000 high-quality radio-occultation (RO) profiles daily over the tropics and subtropics. The initial evaluation of the impact of assimilating COSMIC-2 into NOAA’s Global Forecast System (GFS) showed mixed results, and adjustments to quality control procedures and observation error characteristics had to be made prior to the assimilation of this dataset in the operational configuration in May 2020. Additional changes in the GFS that followed this initial operational implementation resulted in a larger percentage of rejection (~ 90 %) of all RO observations, including COSMIC-2, in the mid-lower troposphere. Since then, two software upgrades directly related to the assimilation of RO bending angle observations were developed. These improvements aimed at optimizing the utilization of COSMIC-2 and other RO observations to improve global weather analyses and forecasts. The first upgrade was implemented operationally in September 2021 and the second one in November 2022. This study describes both RO software upgrades and evaluates the impact of COSMIC-2 with this most recently improved configuration. Specifically, we show that the assimilation of COSMIC-2 observations has a significant impact in improving temperature and winds in the tropics, though benefits also extend to the extra-tropical latitudes.
Abstract
The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root mean square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7 to 0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model's skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. SIC is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions.
Abstract
The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root mean square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7 to 0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model's skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. SIC is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions.
