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Abstract
The influence of the surface latent and surface sensible heat flux on the development and interaction of an idealized extratropical cyclone (termed “primary”) with an upstream cyclone (termed “upstream”) using the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) is analyzed. The primary cyclone develops from an initial perturbation to a baroclinically unstable jet stream, while the upstream cyclone results from Rossby wave dispersion at the surface where a bottom-up style development occurs. The intensity of the upstream cyclone is strongly enhanced by surface latent heat fluxes and, to a lesser degree, by surface sensible heat fluxes. Forward trajectories initiated from the postfrontal sector of the primary cyclone travel south of the upstream anticyclone and feed into the atmospheric river and warm conveyor belt region of the upstream cyclone. Substantial moistening of this airstream is a result of upward surface latent heat flux present in both the primary cyclone’s postfrontal sector and along the southern flank of the anticyclone. Backward trajectories initiated from the same region show that these air parcels originate from a broad area north of both the anticyclone and the primary cyclone in the lower troposphere. The airstream identified represents a new pathway through which dry, descending air that is preconditioned through surface moistening enhances the development of an upstream cyclone through diabatically generated potential vorticity.
Abstract
The influence of the surface latent and surface sensible heat flux on the development and interaction of an idealized extratropical cyclone (termed “primary”) with an upstream cyclone (termed “upstream”) using the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) is analyzed. The primary cyclone develops from an initial perturbation to a baroclinically unstable jet stream, while the upstream cyclone results from Rossby wave dispersion at the surface where a bottom-up style development occurs. The intensity of the upstream cyclone is strongly enhanced by surface latent heat fluxes and, to a lesser degree, by surface sensible heat fluxes. Forward trajectories initiated from the postfrontal sector of the primary cyclone travel south of the upstream anticyclone and feed into the atmospheric river and warm conveyor belt region of the upstream cyclone. Substantial moistening of this airstream is a result of upward surface latent heat flux present in both the primary cyclone’s postfrontal sector and along the southern flank of the anticyclone. Backward trajectories initiated from the same region show that these air parcels originate from a broad area north of both the anticyclone and the primary cyclone in the lower troposphere. The airstream identified represents a new pathway through which dry, descending air that is preconditioned through surface moistening enhances the development of an upstream cyclone through diabatically generated potential vorticity.
Abstract
An analysis of the influence and sensitivity of moisture in an idealized two-dimensional moist semigeostrophic frontogenesis model is presented. A comparison between a dry (relative humidity RH = 0%) version and a moist (RH = 80%) version of the model demonstrates that the impact of moisture is to increase frontogenesis, strengthen the transverse circulation (u ag, w), generate a low-level potential-vorticity anomaly, and develop a low-level jet. The idealized model is compared with a real case simulated with the full-physics three-dimensional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model, establishing good agreement and thereby confirming that the idealized model retains the essential physical processes relevant for improving understanding of midlatitude frontogenesis. Optimal perturbations of mixing ratio are calculated to quantify the circulation response of the model through the computation of singular vectors, which determines the fastest-growing modes of a linearized version of the idealized model. The vertical velocity is found to respond strongly to initial-condition mixing-ratio perturbations such that small changes in moisture lead to large changes in the ascent. The progression of physical processes responsible for this nonlinear growth is (in order) jet/front transverse circulation → moisture convergence ahead of the front → latent heating at mid- to low elevations → reduction in static stability ahead of the front → strengthening of the transverse circulation, and the feedback cycle repeats. Together, these physical processes represent a pathway by which small perturbations of moisture can have a strong impact on a forecast involving midlatitude frontogenesis.
Abstract
An analysis of the influence and sensitivity of moisture in an idealized two-dimensional moist semigeostrophic frontogenesis model is presented. A comparison between a dry (relative humidity RH = 0%) version and a moist (RH = 80%) version of the model demonstrates that the impact of moisture is to increase frontogenesis, strengthen the transverse circulation (u ag, w), generate a low-level potential-vorticity anomaly, and develop a low-level jet. The idealized model is compared with a real case simulated with the full-physics three-dimensional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model, establishing good agreement and thereby confirming that the idealized model retains the essential physical processes relevant for improving understanding of midlatitude frontogenesis. Optimal perturbations of mixing ratio are calculated to quantify the circulation response of the model through the computation of singular vectors, which determines the fastest-growing modes of a linearized version of the idealized model. The vertical velocity is found to respond strongly to initial-condition mixing-ratio perturbations such that small changes in moisture lead to large changes in the ascent. The progression of physical processes responsible for this nonlinear growth is (in order) jet/front transverse circulation → moisture convergence ahead of the front → latent heating at mid- to low elevations → reduction in static stability ahead of the front → strengthening of the transverse circulation, and the feedback cycle repeats. Together, these physical processes represent a pathway by which small perturbations of moisture can have a strong impact on a forecast involving midlatitude frontogenesis.
Abstract
The initial-state sensitivity and optimal perturbation growth for 24- and 36-h forecasts of low-level kinetic energy and precipitation over California during a series of atmospheric river (AR) events that took place in early 2017 are explored using adjoint-based tools from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). This time period was part of the record-breaking winter of 2016–17 in which several high-impact ARs made landfall in California. The adjoint sensitivity indicates that both low-level winds and precipitation are most sensitive to mid- to lower-tropospheric perturbations in the initial state in and near the ARs. A case study indicates that the optimal moist perturbations occur most typically along the subsaturated edges of the ARs, in a warm conveyor belt region. The sensitivity to moisture is largest, followed by temperature and winds. A 1 g kg−1 perturbation to moisture may elicit twice as large a response in kinetic energy and precipitation as a 1 m s−1 perturbation to the zonal or meridional wind. In an average sense, the sensitivity and related optimal perturbations are very similar for the kinetic energy and precipitation response functions. However, on a case-by-case basis, differences in the sensitivity magnitude and optimal perturbation structures result in substantially different forecast perturbations, suggesting that optimal adaptive observing strategies should be metric dependent. While the nonlinear evolved perturbations are usually smaller (by about 20%, on average) than the expected linear perturbations, the optimal perturbations are still capable of producing rapid nonlinear perturbation growth. The positive correlation between sensitivity magnitude and wind speed forecast error or precipitation forecast differences supports the relevance of adjoint-based calculations for predictability studies.
Abstract
The initial-state sensitivity and optimal perturbation growth for 24- and 36-h forecasts of low-level kinetic energy and precipitation over California during a series of atmospheric river (AR) events that took place in early 2017 are explored using adjoint-based tools from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). This time period was part of the record-breaking winter of 2016–17 in which several high-impact ARs made landfall in California. The adjoint sensitivity indicates that both low-level winds and precipitation are most sensitive to mid- to lower-tropospheric perturbations in the initial state in and near the ARs. A case study indicates that the optimal moist perturbations occur most typically along the subsaturated edges of the ARs, in a warm conveyor belt region. The sensitivity to moisture is largest, followed by temperature and winds. A 1 g kg−1 perturbation to moisture may elicit twice as large a response in kinetic energy and precipitation as a 1 m s−1 perturbation to the zonal or meridional wind. In an average sense, the sensitivity and related optimal perturbations are very similar for the kinetic energy and precipitation response functions. However, on a case-by-case basis, differences in the sensitivity magnitude and optimal perturbation structures result in substantially different forecast perturbations, suggesting that optimal adaptive observing strategies should be metric dependent. While the nonlinear evolved perturbations are usually smaller (by about 20%, on average) than the expected linear perturbations, the optimal perturbations are still capable of producing rapid nonlinear perturbation growth. The positive correlation between sensitivity magnitude and wind speed forecast error or precipitation forecast differences supports the relevance of adjoint-based calculations for predictability studies.
Abstract
Given the prohibitive expense of running a global coupled high-resolution model for multiweek forecasts, we explore the feasibility of running a limited-area model forced by a global model on monthly time scales. Specifically, we seek to understand the constraints of the accuracy of lateral boundary conditions (LBCs) produced by NAVGEM on the skill of limited-area COAMPS forecasts. In this study, we analyze simulations of the successive MJO events of November 2011. In the NAVGEM simulations, the effect of ocean boundary conditions are examined, including fixed sea surface temperature (SST), observed SST, and coupled SST with HYCOM. With fixed SST, the second MJO fails to develop, highlighting the importance of the ocean response in the ability to model successive MJO events. Next, we examine the dependence of the regional COAMPS skill on the global model forecast performance. It is found that even when using the inferior but computationally inexpensive uncoupled NAVGEM for LBCs, coupled COAMPS can accurately predict the successive MJO events. A well-resolved atmospheric Rossby wave that slowly propagates westward in the COAMPS domain contributes to increased predictive skill. Ocean coupling and the ability of the model to sufficiently warm the ocean during the convectively suppressed phase also appears to be critical. Last, while COAMPS exhibits a significant moist bias, the sign and magnitude of the vertical and horizontal moisture flux appear to be consistent with reanalysis, a necessary attribute of any model to be used in multiweek MJO prediction.
Abstract
Given the prohibitive expense of running a global coupled high-resolution model for multiweek forecasts, we explore the feasibility of running a limited-area model forced by a global model on monthly time scales. Specifically, we seek to understand the constraints of the accuracy of lateral boundary conditions (LBCs) produced by NAVGEM on the skill of limited-area COAMPS forecasts. In this study, we analyze simulations of the successive MJO events of November 2011. In the NAVGEM simulations, the effect of ocean boundary conditions are examined, including fixed sea surface temperature (SST), observed SST, and coupled SST with HYCOM. With fixed SST, the second MJO fails to develop, highlighting the importance of the ocean response in the ability to model successive MJO events. Next, we examine the dependence of the regional COAMPS skill on the global model forecast performance. It is found that even when using the inferior but computationally inexpensive uncoupled NAVGEM for LBCs, coupled COAMPS can accurately predict the successive MJO events. A well-resolved atmospheric Rossby wave that slowly propagates westward in the COAMPS domain contributes to increased predictive skill. Ocean coupling and the ability of the model to sufficiently warm the ocean during the convectively suppressed phase also appears to be critical. Last, while COAMPS exhibits a significant moist bias, the sign and magnitude of the vertical and horizontal moisture flux appear to be consistent with reanalysis, a necessary attribute of any model to be used in multiweek MJO prediction.
Abstract
In this study, the contribution of low-frequency (>100 days), Madden–Julian oscillation (MJO), and convectively coupled equatorial wave (CCEW) variability to the skill in predicting convection and winds in the tropics at weeks 1–3 is examined. We use subseasonal forecasts from the Navy Earth System Model (NESM); NCEP Climate Forecast System, version 2 (CFSv2); and ECMWF initialized in boreal summer 1999–2015. A technique for performing wavenumber–frequency filtering on subseasonal forecasts is introduced and applied to these datasets. This approach is better able to isolate regional variations in MJO forecast skill than traditional global MJO indices. Biases in the mean state and in the activity of the MJO and CCEWs are smallest in the ECMWF model. The NESM overestimates cloud cover as well as MJO, equatorial Rossby, and mixed Rossby–gravity/tropical depression activity over the west Pacific. The CFSv2 underestimates convectively coupled Kelvin wave activity. The predictive skill of the models at weeks 1–3 is examined by decomposing the forecasts into wavenumber–frequency signals to determine the modes of variability that contribute to forecast skill. All three models have a similar ability to simulate low-frequency variability but large differences in MJO skill are observed. The skill of the NESM and ECMWF model in simulating MJO-related OLR signals at week 2 is greatest over two regions of high MJO activity, the equatorial Indian Ocean and Maritime Continent, and the east Pacific. The MJO in the CFSv2 is too slow and too weak, which results in lower MJO skill in these regions.
Abstract
In this study, the contribution of low-frequency (>100 days), Madden–Julian oscillation (MJO), and convectively coupled equatorial wave (CCEW) variability to the skill in predicting convection and winds in the tropics at weeks 1–3 is examined. We use subseasonal forecasts from the Navy Earth System Model (NESM); NCEP Climate Forecast System, version 2 (CFSv2); and ECMWF initialized in boreal summer 1999–2015. A technique for performing wavenumber–frequency filtering on subseasonal forecasts is introduced and applied to these datasets. This approach is better able to isolate regional variations in MJO forecast skill than traditional global MJO indices. Biases in the mean state and in the activity of the MJO and CCEWs are smallest in the ECMWF model. The NESM overestimates cloud cover as well as MJO, equatorial Rossby, and mixed Rossby–gravity/tropical depression activity over the west Pacific. The CFSv2 underestimates convectively coupled Kelvin wave activity. The predictive skill of the models at weeks 1–3 is examined by decomposing the forecasts into wavenumber–frequency signals to determine the modes of variability that contribute to forecast skill. All three models have a similar ability to simulate low-frequency variability but large differences in MJO skill are observed. The skill of the NESM and ECMWF model in simulating MJO-related OLR signals at week 2 is greatest over two regions of high MJO activity, the equatorial Indian Ocean and Maritime Continent, and the east Pacific. The MJO in the CFSv2 is too slow and too weak, which results in lower MJO skill in these regions.
Abstract
The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.
Abstract
The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.
Abstract
A high-impact atmospheric river (AR) event that made landfall on the U.S. West Coast on Valentine’s Day of 2019 and produced widespread flooding in California is examined. The U.S. Naval Research Laboratory cloud resolving and high-resolution Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) captures the main features impacting the life cycle and structure of the Valentine’s Day AR. Analysis of the model-simulated AR reveals the complex processes leading up to the initial northeastward surge of the water vapor and enhanced near-surface flow associated with this AR. These include the preexistence of a mesoscale cold-core kona low, a mesoscale anticyclone, and a strong low-level convergence in the corridor between the kona low and mesoscale anticyclone where the environment becomes supersaturated in a region of weak vertical wind shear. Model sensitivity experiments show that the eastward progression and magnitude of the AR water vapor surge are strongly sensitive to the magnitude of kona low circulation. Experiments with the kona low circulation amplitude reduced to less than 25% showed that the AR is not able to reach the U.S. West Coast. These results help to identify key new aspects of an important player—the kona low—and its significant contributions to the overall AR characteristics of this particular observed event.
Abstract
A high-impact atmospheric river (AR) event that made landfall on the U.S. West Coast on Valentine’s Day of 2019 and produced widespread flooding in California is examined. The U.S. Naval Research Laboratory cloud resolving and high-resolution Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) captures the main features impacting the life cycle and structure of the Valentine’s Day AR. Analysis of the model-simulated AR reveals the complex processes leading up to the initial northeastward surge of the water vapor and enhanced near-surface flow associated with this AR. These include the preexistence of a mesoscale cold-core kona low, a mesoscale anticyclone, and a strong low-level convergence in the corridor between the kona low and mesoscale anticyclone where the environment becomes supersaturated in a region of weak vertical wind shear. Model sensitivity experiments show that the eastward progression and magnitude of the AR water vapor surge are strongly sensitive to the magnitude of kona low circulation. Experiments with the kona low circulation amplitude reduced to less than 25% showed that the AR is not able to reach the U.S. West Coast. These results help to identify key new aspects of an important player—the kona low—and its significant contributions to the overall AR characteristics of this particular observed event.
Abstract
Atmospheric rivers, often associated with impactful weather along the west coast of North America, can be a challenge to forecast even on short time scales. This is attributed, at least in part, to the scarcity of eastern Pacific in situ observations. We examine the impact of assimilating dropsonde observations collected during the Atmospheric River (AR) Reconnaissance 2018 field program on the Navy Global Environmental Model (NAVGEM) analyses and forecasts. We compare NAVGEM’s representation of the ARs to the observations, and examine whether the observation–background difference statistics are similar to the observation error variance specified in the data assimilation system. Forecast sensitivity observation impact is determined for each dropsonde variable, and compared to the impacts of the North American radiosonde network. We find that the reconnaissance soundings have significant beneficial impact, with per observation impact more than double that of the North American radiosonde network. Temperature and wind observations have larger total and per observation impact than moisture observations. In our experiment, the 24-h global forecast error reduction from the reconnaissance soundings can be comparable to the reduction from the North American radiosonde network for the field program dates that include at least two flights.
Abstract
Atmospheric rivers, often associated with impactful weather along the west coast of North America, can be a challenge to forecast even on short time scales. This is attributed, at least in part, to the scarcity of eastern Pacific in situ observations. We examine the impact of assimilating dropsonde observations collected during the Atmospheric River (AR) Reconnaissance 2018 field program on the Navy Global Environmental Model (NAVGEM) analyses and forecasts. We compare NAVGEM’s representation of the ARs to the observations, and examine whether the observation–background difference statistics are similar to the observation error variance specified in the data assimilation system. Forecast sensitivity observation impact is determined for each dropsonde variable, and compared to the impacts of the North American radiosonde network. We find that the reconnaissance soundings have significant beneficial impact, with per observation impact more than double that of the North American radiosonde network. Temperature and wind observations have larger total and per observation impact than moisture observations. In our experiment, the 24-h global forecast error reduction from the reconnaissance soundings can be comparable to the reduction from the North American radiosonde network for the field program dates that include at least two flights.
Abstract
High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of Earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line analysis correction-based additive inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7- and 14-day IVT bias by 16%–17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the South Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveals areas with compensating errors.
Abstract
High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of Earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line analysis correction-based additive inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7- and 14-day IVT bias by 16%–17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the South Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveals areas with compensating errors.
Abstract
Moist static energy (MSE) and ocean heat content (OHC) in the tropics are inextricably linked. The processes by which sources and sinks of OHC modulate column integrated MSE in the Indian Ocean (IO) are explored through a reformulation of the MSE budget using atmosphere and ocean reanalysis data. In the reframed MSE budget, interfacial air–sea turbulent and radiative fluxes are replaced for information on upper ocean dynamics, thus “mooring” the MSE tendency to the subsurface ocean. On subseasonal time scales, ocean forcing is largely responsible for the amplification of MSE anomalies across the IO, with basin average growth rates of 10% day−1. Local OHC depletion is the leading contributor to anomalous MSE amplification with average rates of 12% day−1. Along the equator, MSE is amplified by OHC vertical advection. Ocean forcing only weakly reduces the propagation tendency of MSE anomalies (−2% day−1), with propagation predominantly resulting from atmosphere forcing (10% day−1). OHC in the IO acts as an MSE reservoir that is expended during periods of enhanced intraseasonal atmosphere convection and recharged during periods of suppressed convection. Because OHC is an MSE source during enhanced intraseasonal convection periods, it largely offsets the negative MSE tendency produced by horizontal advection in the atmosphere. The opposite effect occurs during suppressed convection periods, where OHC is a sink of MSE and counters the positive MSE tendency produced by horizontal advection in the atmosphere.
Abstract
Moist static energy (MSE) and ocean heat content (OHC) in the tropics are inextricably linked. The processes by which sources and sinks of OHC modulate column integrated MSE in the Indian Ocean (IO) are explored through a reformulation of the MSE budget using atmosphere and ocean reanalysis data. In the reframed MSE budget, interfacial air–sea turbulent and radiative fluxes are replaced for information on upper ocean dynamics, thus “mooring” the MSE tendency to the subsurface ocean. On subseasonal time scales, ocean forcing is largely responsible for the amplification of MSE anomalies across the IO, with basin average growth rates of 10% day−1. Local OHC depletion is the leading contributor to anomalous MSE amplification with average rates of 12% day−1. Along the equator, MSE is amplified by OHC vertical advection. Ocean forcing only weakly reduces the propagation tendency of MSE anomalies (−2% day−1), with propagation predominantly resulting from atmosphere forcing (10% day−1). OHC in the IO acts as an MSE reservoir that is expended during periods of enhanced intraseasonal atmosphere convection and recharged during periods of suppressed convection. Because OHC is an MSE source during enhanced intraseasonal convection periods, it largely offsets the negative MSE tendency produced by horizontal advection in the atmosphere. The opposite effect occurs during suppressed convection periods, where OHC is a sink of MSE and counters the positive MSE tendency produced by horizontal advection in the atmosphere.
