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Abstract
Realistic initialization of the land surface is important to produce accurate NWP forecasts. Therefore, making use of available observations is essential when estimating the surface state. In this work, sequential land surface data assimilation of soil variables is replaced with an offline cycling method. To obtain the best possible initial state for the lower boundary of the NWP system, the land surface model is rerun between forecasts with an analyzed atmospheric forcing. We found a relative reduction of 2-m temperature root-mean-square errors and mean errors of 6% and 12%, respectively, and 4.5% and 11% for 2-m specific humidity. During a convective event, the system was able to produce useful (fractions skill score greater than the uniform forecast) forecasts [above 30 mm (12 h)−1] down to a 100-km length scale where the reference failed to do so below 200 km. The different precipitation forcing caused differences in soil moisture fields that persisted for several weeks and consequently impacted the surface fluxes of heat and moisture and the forecasts of screen level parameters. The experiments also indicate diurnal- and weather-dependent variations of the forecast errors that give valuable insight on the role of initial land surface conditions and the land–atmosphere interactions in southern Scandinavia.
Abstract
Realistic initialization of the land surface is important to produce accurate NWP forecasts. Therefore, making use of available observations is essential when estimating the surface state. In this work, sequential land surface data assimilation of soil variables is replaced with an offline cycling method. To obtain the best possible initial state for the lower boundary of the NWP system, the land surface model is rerun between forecasts with an analyzed atmospheric forcing. We found a relative reduction of 2-m temperature root-mean-square errors and mean errors of 6% and 12%, respectively, and 4.5% and 11% for 2-m specific humidity. During a convective event, the system was able to produce useful (fractions skill score greater than the uniform forecast) forecasts [above 30 mm (12 h)−1] down to a 100-km length scale where the reference failed to do so below 200 km. The different precipitation forcing caused differences in soil moisture fields that persisted for several weeks and consequently impacted the surface fluxes of heat and moisture and the forecasts of screen level parameters. The experiments also indicate diurnal- and weather-dependent variations of the forecast errors that give valuable insight on the role of initial land surface conditions and the land–atmosphere interactions in southern Scandinavia.
Abstract
The prediction of snow accumulation remains a forecasting challenge. While the adoption of ensemble numerical weather prediction has enabled the development of probabilistic guidance, the challenges associated with snow accumulation, particularly snow-to-liquid ratio (SLR), still remain when building snow-accumulation tools. In operations, SLR is generally assumed to either fit a simple mathematical relationship or conform to a historic average. In this paper, the impacts of the choice of SLR on ensemble snow forecasts are tested. Ensemble forecasts from the nine-member High-Resolution Rapid Refresh Ensemble (HRRRE) were used to create 24-h snowfall forecasts for five snowfall events associated with winter cyclones. These snowfall forecasts were derived from model liquid precipitation forecasts using five SLR relationships. These forecasts were evaluated against daily new snowfall observations from the Community Collaborative Rain Hail and Snow network. The results of this analysis show that the forecast error associated with individual members is similar to the error associated with choice of SLR. The SLR with the lowest forecast error showed regional agreement across nearby observations. This suggests that, while there is no one SLR that works best everywhere, it may be possible to improve ensemble snow forecasts if regions where SLRs perform best can be determined ahead of time. The implications of these findings for future ensemble snowfall tools will be discussed.
Significance Statement
Snowfall prediction remains a challenge. Computer models are used to address the inherent uncertainty in forecasts. This uncertainty includes aspects like the location and rate of snowfall. Meteorologists run multiple similar computer models to understand the range of possible weather outcomes. One aspect of uncertainty is the snow-to-liquid ratio, or the ratio of snow depth to the amount of liquid water it melts into. This study tests how common predictions of snow-to-liquid ratio impact snowfall forecasts. The results show that snow-to-liquid ratio choices are as impactful as the models’ differing snow rate or snow location forecasts, and that no particular snow-to-liquid ratio is most accurate. These results underscore the importance of better snow-to-liquid ratio prediction to improve snowfall forecasts.
Abstract
The prediction of snow accumulation remains a forecasting challenge. While the adoption of ensemble numerical weather prediction has enabled the development of probabilistic guidance, the challenges associated with snow accumulation, particularly snow-to-liquid ratio (SLR), still remain when building snow-accumulation tools. In operations, SLR is generally assumed to either fit a simple mathematical relationship or conform to a historic average. In this paper, the impacts of the choice of SLR on ensemble snow forecasts are tested. Ensemble forecasts from the nine-member High-Resolution Rapid Refresh Ensemble (HRRRE) were used to create 24-h snowfall forecasts for five snowfall events associated with winter cyclones. These snowfall forecasts were derived from model liquid precipitation forecasts using five SLR relationships. These forecasts were evaluated against daily new snowfall observations from the Community Collaborative Rain Hail and Snow network. The results of this analysis show that the forecast error associated with individual members is similar to the error associated with choice of SLR. The SLR with the lowest forecast error showed regional agreement across nearby observations. This suggests that, while there is no one SLR that works best everywhere, it may be possible to improve ensemble snow forecasts if regions where SLRs perform best can be determined ahead of time. The implications of these findings for future ensemble snowfall tools will be discussed.
Significance Statement
Snowfall prediction remains a challenge. Computer models are used to address the inherent uncertainty in forecasts. This uncertainty includes aspects like the location and rate of snowfall. Meteorologists run multiple similar computer models to understand the range of possible weather outcomes. One aspect of uncertainty is the snow-to-liquid ratio, or the ratio of snow depth to the amount of liquid water it melts into. This study tests how common predictions of snow-to-liquid ratio impact snowfall forecasts. The results show that snow-to-liquid ratio choices are as impactful as the models’ differing snow rate or snow location forecasts, and that no particular snow-to-liquid ratio is most accurate. These results underscore the importance of better snow-to-liquid ratio prediction to improve snowfall forecasts.
Abstract
Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models.
Abstract
Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models.
Abstract
In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional time scales covered by weather forecasting in the Philippines, this study has examined the multiweek (i.e., from week 1 to week 4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely, the NCEP Climate Forecast System version 2 (CFSv2), the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and the NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 6 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multiweek TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p < 0.05) positional bias toward the right of observed tracks beyond week 1, and that they tend to propagate slower than observations especially in week 1 and week 2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multiweek TC forecasts in the Philippines.
Abstract
In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional time scales covered by weather forecasting in the Philippines, this study has examined the multiweek (i.e., from week 1 to week 4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely, the NCEP Climate Forecast System version 2 (CFSv2), the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and the NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 6 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multiweek TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p < 0.05) positional bias toward the right of observed tracks beyond week 1, and that they tend to propagate slower than observations especially in week 1 and week 2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multiweek TC forecasts in the Philippines.
Abstract
Deep-layer vertical wind shear and midtropospheric relative humidity (RH) are explored in and around environments of all intensifying North Atlantic tropical cyclones (TCs) between 1980 and 2021 using reanalysis data. Shear and RH are averaged within the standard environmental annulus of 200–800 km, along with a 100–600-km annulus, and a 0–250-km radius to represent the inner core and TC itself. Distributions of shear and RH at onset along with a time series of evolution from 48 h prior to and after onset of three different intensification rates, slight [5–10 kt (24 h)−1; 1 kt ≈ 0.51 m s−1], moderate [15–25 kt (24 h)−1], and rapid [≥30 kt (24 h)−1], are analyzed. RH is also investigated within different shear environments and in shear-relative quadrants around the storm. While low shear and high RH are found to be most favorable for rapid intensification (RI), there is still a significant probability that RI will occur within less favorable environments. RI cases decrease in 850–200-hPa shear in the 24 h leading up to RI, whereas slight intensification cases increase, which is evident in both the standard shear and a shallower layer at 48 h prior to onset. The inner-core RH for RI increases prior to onset whereas it decreases in the environments. RH analysis by shear-relative quadrants demonstrates the importance of moistening in the upshear-right quadrant before onset of RI. Results indicate the potential value of multiple annuli and shear-relative analysis for moisture and a shallower, 925–400-hPa layer for shear in RI forecasting.
Significance Statement
The purpose of this study was to investigate the wind and moisture around different areas of intensifying North Atlantic tropical cyclones between 1980 and 2021 using reanalysis data. Average wind and moisture evolve differently around the onset of different intensification rates as well as in different near and far regions from the storm center. These results provide additional indicators that forecasters may consider when examining how the environment around the storm and the situation in the inner core may influence its future intensity.
Abstract
Deep-layer vertical wind shear and midtropospheric relative humidity (RH) are explored in and around environments of all intensifying North Atlantic tropical cyclones (TCs) between 1980 and 2021 using reanalysis data. Shear and RH are averaged within the standard environmental annulus of 200–800 km, along with a 100–600-km annulus, and a 0–250-km radius to represent the inner core and TC itself. Distributions of shear and RH at onset along with a time series of evolution from 48 h prior to and after onset of three different intensification rates, slight [5–10 kt (24 h)−1; 1 kt ≈ 0.51 m s−1], moderate [15–25 kt (24 h)−1], and rapid [≥30 kt (24 h)−1], are analyzed. RH is also investigated within different shear environments and in shear-relative quadrants around the storm. While low shear and high RH are found to be most favorable for rapid intensification (RI), there is still a significant probability that RI will occur within less favorable environments. RI cases decrease in 850–200-hPa shear in the 24 h leading up to RI, whereas slight intensification cases increase, which is evident in both the standard shear and a shallower layer at 48 h prior to onset. The inner-core RH for RI increases prior to onset whereas it decreases in the environments. RH analysis by shear-relative quadrants demonstrates the importance of moistening in the upshear-right quadrant before onset of RI. Results indicate the potential value of multiple annuli and shear-relative analysis for moisture and a shallower, 925–400-hPa layer for shear in RI forecasting.
Significance Statement
The purpose of this study was to investigate the wind and moisture around different areas of intensifying North Atlantic tropical cyclones between 1980 and 2021 using reanalysis data. Average wind and moisture evolve differently around the onset of different intensification rates as well as in different near and far regions from the storm center. These results provide additional indicators that forecasters may consider when examining how the environment around the storm and the situation in the inner core may influence its future intensity.
Abstract
This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1.
Abstract
This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1.
Abstract
A modification to the mixing length formulation in a planetary boundary layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational Global Forecast System performs well in terms of the reduced root-mean-square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of the operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed.
Significance Statement
A modification to the mixing length formulation in a PBL scheme is described, which improves the intensity forecast of tropical cyclones simulated in the Hurricane Analysis and Forecast System (HAFS). Retrospective tests indicate that the modification can reduce the root-mean-square error and bias of the simulated TC intensity by 5%–10% and 50%, respectively. This modification has been implemented in one of the operational configurations of HAFS, version 1, at NCEP, improving the hurricane model guidance.
Abstract
A modification to the mixing length formulation in a planetary boundary layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational Global Forecast System performs well in terms of the reduced root-mean-square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of the operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed.
Significance Statement
A modification to the mixing length formulation in a PBL scheme is described, which improves the intensity forecast of tropical cyclones simulated in the Hurricane Analysis and Forecast System (HAFS). Retrospective tests indicate that the modification can reduce the root-mean-square error and bias of the simulated TC intensity by 5%–10% and 50%, respectively. This modification has been implemented in one of the operational configurations of HAFS, version 1, at NCEP, improving the hurricane model guidance.
Abstract
While prior research has shown that characteristics of the supercell environment can indicate the likelihood of tornadogenesis, it is common for tornadic and nontornadic supercells to coexist in seemingly similar environments. Thus, some small-scale factors must support tornadogenesis in some supercells and not in others. In this study we examined polarimetric radar signatures of proximate pretornadic and nontornadic supercells in seemingly similar environments to determine if these radar signatures can indicate which proximate supercells are pretornadic and which are nontornadic. We gathered a collection of proximity supercell groups and developed a method to quantify environmental similarity between storms. Using this method, we selected pretornadic–nontornadic supercell pairs in close proximity in space and time having the most similar environments. These pairs were run through an automated tracking algorithm that quantifies polarimetric signatures in each supercell. Supercells with larger differential reflectivity (Z DR) column areas were more likely to become tornadic within the next 30 min compared to neighboring supercells with smaller Z DR column areas. In about two-thirds of pairs, the pretornadic supercell had a larger Z DR column area than the nontornadic supercell prior to its maximum low-level rotation, which is consistent with much prior work. The Z DR arcs could not discriminate between pretornadic and nontornadic supercells, and hailfall area was larger in pretornadic supercells. The separation distance between the specific differential phase (K DP) foot and the Z DR arc was larger in pretornadic supercells, yet was a limited result due to the small sample size used for comparison.
Significance Statement
Atmospheric conditions often indicate whether certain thunderstorms will produce tornadoes. However, sometimes multiple thunderstorms exist in a similar environment, and some produce tornadoes while others do not. Weather radar can identify signatures within thunderstorms that may give some indication of vertical motion, size sorting, and precipitation distributions. When multiple thunderstorms exist in a similar environment, there may be differences in these radar signatures that may indicate which thunderstorms are most likely to become tornadic. The key finding from this study is that pretornadic storms have larger radar-inferred updraft areas than neighboring nontornadic storms.
Abstract
While prior research has shown that characteristics of the supercell environment can indicate the likelihood of tornadogenesis, it is common for tornadic and nontornadic supercells to coexist in seemingly similar environments. Thus, some small-scale factors must support tornadogenesis in some supercells and not in others. In this study we examined polarimetric radar signatures of proximate pretornadic and nontornadic supercells in seemingly similar environments to determine if these radar signatures can indicate which proximate supercells are pretornadic and which are nontornadic. We gathered a collection of proximity supercell groups and developed a method to quantify environmental similarity between storms. Using this method, we selected pretornadic–nontornadic supercell pairs in close proximity in space and time having the most similar environments. These pairs were run through an automated tracking algorithm that quantifies polarimetric signatures in each supercell. Supercells with larger differential reflectivity (Z DR) column areas were more likely to become tornadic within the next 30 min compared to neighboring supercells with smaller Z DR column areas. In about two-thirds of pairs, the pretornadic supercell had a larger Z DR column area than the nontornadic supercell prior to its maximum low-level rotation, which is consistent with much prior work. The Z DR arcs could not discriminate between pretornadic and nontornadic supercells, and hailfall area was larger in pretornadic supercells. The separation distance between the specific differential phase (K DP) foot and the Z DR arc was larger in pretornadic supercells, yet was a limited result due to the small sample size used for comparison.
Significance Statement
Atmospheric conditions often indicate whether certain thunderstorms will produce tornadoes. However, sometimes multiple thunderstorms exist in a similar environment, and some produce tornadoes while others do not. Weather radar can identify signatures within thunderstorms that may give some indication of vertical motion, size sorting, and precipitation distributions. When multiple thunderstorms exist in a similar environment, there may be differences in these radar signatures that may indicate which thunderstorms are most likely to become tornadic. The key finding from this study is that pretornadic storms have larger radar-inferred updraft areas than neighboring nontornadic storms.
Abstract
Assimilating radar reflectivity into convective-scale NWP models remains a challenging topic in radar data assimilation. A primary reason is that the reflectivity forward observation operator is highly nonlinear. To address this challenge, a power transformation function is applied to the WRF Model’s hydrometeor and water vapor mixing ratio variables in this study. Three 3D variational data assimilation experiments are performed and compared for five high-impact weather events that occurred in 2019: (i) a control experiment that assimilates reflectivity using the original hydrometeor mixing ratios as control variables, (ii) an experiment that assimilates reflectivity using power-transformed hydrometeor mixing ratios as control variables, and (iii) an experiment that assimilates reflectivity and retrieved pseudo–water vapor observations using power-transformed hydrometeor and water vapor mixing ratios (qυ ) as control variables. Both qualitative and quantitative evaluations are performed for 0–3-h forecasts from the five cases. The analysis and forecast performance in the two experiments with power-transformed mixing ratios is better than the control experiment. Notably, the assimilation of pseudo–water vapor with power-transformed qυ as an additional control variable is found to improve the performance of the analysis and short-term forecasts for all cases. In addition, the convergence rate of the cost function minimization for the two experiments that use the power transformation is faster than that of the control experiments.
Significance Statement
The effective use of radar reflectivity observations in any data assimilation scheme remains an important research topic because reflectivity observations explicitly include information about hydrometeors and also implicitly include information about the distribution of moisture within storms. However, it is difficult to assimilate reflectivity because the reflectivity forward observation operator is highly nonlinear. This study seeks to identify a more effective way to assimilate reflectivity into a convective-scale NWP model to improve the accuracy of predictions of high-impact weather events.
Abstract
Assimilating radar reflectivity into convective-scale NWP models remains a challenging topic in radar data assimilation. A primary reason is that the reflectivity forward observation operator is highly nonlinear. To address this challenge, a power transformation function is applied to the WRF Model’s hydrometeor and water vapor mixing ratio variables in this study. Three 3D variational data assimilation experiments are performed and compared for five high-impact weather events that occurred in 2019: (i) a control experiment that assimilates reflectivity using the original hydrometeor mixing ratios as control variables, (ii) an experiment that assimilates reflectivity using power-transformed hydrometeor mixing ratios as control variables, and (iii) an experiment that assimilates reflectivity and retrieved pseudo–water vapor observations using power-transformed hydrometeor and water vapor mixing ratios (qυ ) as control variables. Both qualitative and quantitative evaluations are performed for 0–3-h forecasts from the five cases. The analysis and forecast performance in the two experiments with power-transformed mixing ratios is better than the control experiment. Notably, the assimilation of pseudo–water vapor with power-transformed qυ as an additional control variable is found to improve the performance of the analysis and short-term forecasts for all cases. In addition, the convergence rate of the cost function minimization for the two experiments that use the power transformation is faster than that of the control experiments.
Significance Statement
The effective use of radar reflectivity observations in any data assimilation scheme remains an important research topic because reflectivity observations explicitly include information about hydrometeors and also implicitly include information about the distribution of moisture within storms. However, it is difficult to assimilate reflectivity because the reflectivity forward observation operator is highly nonlinear. This study seeks to identify a more effective way to assimilate reflectivity into a convective-scale NWP model to improve the accuracy of predictions of high-impact weather events.
Abstract
The object-based verification procedure described in a recent paper by Duda and Turner was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast–observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced overforecasting bias for medium- and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely overforecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts.
Significance Statement
This work builds upon the authors’ prior work in assessing model forecast quality using an alternative verification method—object-based verification. In this paper we verified two versions of the same model (one an upgrade from the other) that were making forecasts covering the same time window, using the object-based verification method. We found that the updated model was not statistically significantly better, although there were indications it performed better in certain aspects such as capturing the change in the number of storms during the daytime. We were able to identify specific problem areas in the models, which helps us direct model developers in their efforts to further improve the model.
Abstract
The object-based verification procedure described in a recent paper by Duda and Turner was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast–observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced overforecasting bias for medium- and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely overforecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts.
Significance Statement
This work builds upon the authors’ prior work in assessing model forecast quality using an alternative verification method—object-based verification. In this paper we verified two versions of the same model (one an upgrade from the other) that were making forecasts covering the same time window, using the object-based verification method. We found that the updated model was not statistically significantly better, although there were indications it performed better in certain aspects such as capturing the change in the number of storms during the daytime. We were able to identify specific problem areas in the models, which helps us direct model developers in their efforts to further improve the model.
