Abstract

A regional-scale Observing System Simulation Experiment is used to examine how changes in the horizontal covariance localization radius employed during the assimilation of infrared brightness temperature observations in an ensemble Kalman filter assimilation system impacts the accuracy of atmospheric analyses and short-range model forecasts. The case study tracks the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results indicate that assimilating 8.5-μm brightness temperatures improves the cloud analysis and forecast accuracy, but has the tendency to degrade the water vapor mixing ratio and thermodynamic fields unless a small localization radius is used. Vertical cross sections showed that varying the localization radius had a minimal impact on the shape of the analysis increments; however, their magnitude consistently increased with increasing localization radius. By the end of the assimilation period, the moisture, temperature, cloud, and wind errors generally decreased with decreasing localization radius and became similar to the Control case in which only conventional observations were assimilated if the shortest localization radius was used. Short-range ensemble forecasts showed that the large positive impact of the infrared observations on the final cloud analysis diminished rapidly during the forecast period, which indicates that it is difficult to maintain beneficial changes to the cloud analysis if the moisture and thermodynamic forcing controlling the cloud evolution are not simultaneously improved. These results show that although assimilation of infrared observations consistently improves the cloud field regardless of the length of the localization radius, it may be necessary to use a smaller radius to also improve the accuracy of the moisture and thermodynamic fields.

Abstract

In this study, the ability of different combinations of bulk cloud microphysics and planetary boundary layer (PBL) parameterization schemes implemented in the Weather Research and Forecasting Model to realistically simulate the wide variety of cloud types associated with an extratropical cyclone is examined. An ensemble of high-resolution model simulations was constructed for this case using four microphysics and two PBL schemes characterized by different levels of complexity. Simulated cloud properties, including cloud optical thickness, cloud water path, cloud-top pressure, and radiative cloud phase, were subsequently compared to cloud data from three Moderate Resolution Imaging Spectroradiometer (MODIS) overpasses across different portions of the domain. A detailed comparison of the simulated datasets revealed that the PBL and cloud microphysics schemes both exerted a strong influence on the spatial distribution and physical properties of the simulated cloud fields. In particular, the low-level cloud properties were found to be very sensitive to the PBL scheme while the upper-level clouds were sensitive to both the microphysics and PBL schemes. Overall, the simulated cloud properties were broadly similar to the MODIS observations, with the most realistic cloud fields produced by the more sophisticated parameterization schemes.

Abstract

An analysis of the composite large-scale circulation associated with periods of enhanced (active) or diminished (inactive) cyclogenesis in the subtropical central and eastern Pacific Ocean is presented. Composites were constructed using surface and tropospheric analyses from the ECMWF Tropical Ocean Global Atmosphere (TOGA) dataset for 10 Northern Hemisphere cool seasons (1986–96). Active periods of subtropical cyclogenesis were defined to be periods in which two or more cyclones developed in close succession to each other, while inactive periods were defined to be periods of at least 10-days duration during which no cyclones with a subtropical origin were present in the Pacific basin.

The analysis revealed that the occurrence of subtropical cyclones in the central and eastern Pacific Ocean is strongly linked to the strength and location of the Asian jet, with active periods characterized by a weaker, zonally retracted Asian jet while inactive periods are characterized by a stronger, zonally elongated Asian jet. Consideration of the stationary wavenumber, K _s , showed that the strong, zonally elongated jet characterizing inactive periods produced a continuous waveguide across the Pacific basin that severely limited the equatorward propagation of upper-level cyclones into the subtropical Pacific. However, the zonally retracted jet during active periods was associated with a poorly organized, or “leakier,” waveguide across the Pacific, which produced a decidedly more favorable situation for the equatorward propagation of upper-level cyclones leaving the exit region of the Asian jet.

Outgoing longwave radiation data were used to explore the potential link between anomalous convection in the tropical Pacific and the occurrence of active and inactive periods. A detailed analysis of each active and inactive period revealed that only 55% of the periods were characterized by the theoretically expected distribution of anomalous convection across the tropical Pacific (deemed “correct”) and that 30% of the periods were actually characterized by the exact opposite distribution (deemed “incorrect”). During correct active and correct inactive periods, Rossby wave dispersion away from anomalous tropical convection in the central Pacific is associated with an extratropical response resembling the Pacific–North American pattern. Further analysis revealed that the lack of subtropical cyclones during most incorrect inactive periods was associated with a strengthened and zonally elongated Asian jet. The observed broadening and weakening of the Asian jet that occurs during the transition to incorrect active periods suggests that barotropic energy conversions may play an important role in fostering a large-scale environment conducive to the frequent development of subtropical cyclones during incorrect active periods.

Abstract

Ten years of surface and upper-air analyses from the ECMWF Tropical Ocean Global Atmosphere (TOGA) dataset were used to construct a synoptic climatology of kona storms in the subtropical central and eastern Pacific Ocean. Within a sample of 115 cyclones that predominantly occurred during the Northern Hemisphere cool season, three distinct types of kona storms were identified: cold-frontal cyclogenesis (CFC) cyclones, cold-frontal cyclogenesis/trade wind easterlies (CT) cyclones, and trade wind easterlies (TWE) cyclones. Of the three types, CFC cyclones were found to be the most common type of kona storm, while CT and TWE cyclones occur much less frequently.

The geographical distribution, propagation characteristics, and the monthly and interannual variability in the number of kona storms are presented. Kona storms initially develop across a large portion of the subtropical Pacific, with the greatest concentration of kona storms found within a southwest-to-northeast-oriented band from west of Hawaii to 40°N, 140°W. A distinct latitudinal stratification was evident for each type of kona storm, with CFC, CT, and TWE cyclones each more likely to initially develop at successively lower latitudes. The analysis reveals that kona storms can propagate in any direction but exhibit a clear preference to propagate toward the northeast. Use of the multivariate ENSO index indicates that the number of kona storms that develop during each cool season is not correlated to the phase of ENSO.

An analysis of the composite structure and evolution of each type of kona storm revealed some common and some unique characteristics. Development of the surface cyclone in all types results from the intrusion of an upper-level disturbance of extratropical origin into the subtropics, although differences in the initial structure and subsequent evolution of the 300-hPa trough were noted for each type of kona storm. The analysis also revealed that relatively weak 300-hPa winds are present throughout the evolution of each type of kona storm and that the composite kona storm tends to be nestled along the southern boundary of a region of higher surface pressure during the mature stage of its evolution. The development of robust ridges in the 300-hPa geopotential and 1000–500-hPa thickness fields downstream of the composite surface cyclone were noteworthy features that characterized the evolution of all kona storms, the latter feature strongly suggesting that these disturbances are fundamentally baroclinic in nature.

Abstract

The life cycle of a central Pacific cyclone, characterized by a 48-h interval of rapid fluctuation in its intensity, is examined. The cyclone of interest underwent a period of explosive cyclogenesis from 1200 UTC 4 November to 1200 UTC 5 November 1986, followed 12 h later by a period of unusually rapid decay. Output from a numerical simulation of this event, run using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), is used to perform a piecewise potential vorticity (PV) inversion in order to diagnose the life cycle of this unusual cyclone.

The analysis reveals that the presence of lower-tropospheric frontogenetic forcing in an environment characterized by reduced static stability (as measured by high values of the K index) produced a burst of heavy precipitation during the development stage of the cyclone's life cycle. The associated latent heat release produced a substantial diabatic PV anomaly in the middle troposphere that was, in turn, responsible for the majority of the lower-tropospheric height falls associated with the explosive cyclogenesis. Subsequent height rises during the rapid cyclolysis stage resulted from the northward migration of the surface cyclone into a perturbation geopotential ridge associated with a negative tropopause-level PV anomaly. This feature developed rapidly in response to the southeastward migration of a preexisting, upstream negative PV anomaly and the production of a second negative tropopause-level PV anomaly to the north of the surface cyclone. This latter feature was a diabatic consequence of the latent heat release that fueled the explosive development. Thus, the very latent heat release that assisted in the rapid development of the cyclone also played an important role in its subsequent decay. It is suggested that such a life cycle may represent an example of a “self- destroying” system.

Abstract

Ensemble data assimilation experiments were performed to assess the ability of satellite all-sky infrared brightness temperatures and different bias correction (BC) predictors to improve the accuracy of short-range forecasts used as the model background during each assimilation cycle. Satellite observations sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a 3-day period. Linear and nonlinear conditional biases were removed from the infrared observations using a Taylor series polynomial expansion of the observation-minus-background departures and BC predictors sensitive to clouds and water vapor or to variations in the satellite zenith angle. Assimilating the all-sky infrared brightness temperatures without BC degraded the forecast accuracy based on comparisons to radiosonde observations. Removal of the linear and nonlinear conditional biases from the satellite observations substantially improved the results, with predictors sensitive to the location of the cloud top having the largest impact, especially when higher-order nonlinear BC terms were used. Overall, experiments employing the observed cloud-top height or observed brightness temperature as the bias predictor had the smallest water vapor, cloud, and wind speed errors, while also having less degradation to temperatures than occurred when using other predictors. The forecast errors were smaller during these experiments because the cloud-height-sensitive BC predictors were able to more effectively remove the large conditional biases for lower brightness temperatures associated with a deficiency in upper-level clouds in the model background.

Abstract

In this study, the ability of several cloud microphysical and planetary boundary layer parameterization schemes to accurately simulate cloud characteristics within 4-km grid-spacing ensemble forecasts over the contiguous United States was evaluated through comparison of synthetic Geostationary Operational Environmental Satellite (GOES) infrared brightness temperatures with observations. Four double-moment microphysics schemes and five planetary boundary layer (PBL) schemes were evaluated. Large differences were found in the simulated cloud cover, especially in the upper troposphere, when using different microphysics schemes. Overall, the results revealed that the Milbrandt–Yau and Morrison microphysics schemes tended to produce too much upper-level cloud cover, whereas the Thompson and the Weather Research and Forecasting Model (WRF) double-moment 6-class (WDM6) microphysics schemes did not contain enough high clouds. Smaller differences occurred in the cloud fields when using different PBL schemes, with the greatest spread in the ensemble statistics occurring during and after daily peak heating hours. Results varied somewhat depending upon the verification method employed, which indicates the importance of using a suite of verification tools when evaluating high-resolution model performance. Finally, large differences between the various microphysics and PBL schemes indicate that large uncertainties remain in how these schemes represent subgrid-scale processes.

Abstract

The evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

Abstract

Synthetic infrared brightness temperatures (BTs) derived from a high-resolution Weather Research and Forecasting (WRF) model simulation over the contiguous United States are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) observations to assess the accuracy of the model-simulated cloud field. A sophisticated forward radiative transfer model (RTM) is used to compute the synthetic MODIS observations. A detailed comparison of synthetic and real MODIS 11-μm BTs revealed that the model simulation realistically depicts the spatial characteristics of the observed cloud features. Brightness temperature differences (BTDs) computed for 8.5–11 and 11–12 μm indicate that the combined numerical model–RTM system realistically treats the radiative properties associated with optically thin cirrus clouds. For instance, much larger 11–12-μm BTDs occurred within thin clouds surrounding optically thicker, mesoscale cloud features. Although the simulated and observed BTD probability distributions for optically thin cirrus clouds had a similar range of positive values, the synthetic 11-μm BTs were much colder than observed. Previous studies have shown that MODIS cloud optical thickness values tend to be too large for thin cirrus clouds, which contributed to the apparent cold BT bias in the simulated thin cirrus clouds. Errors are substantially reduced after accounting for the observed optical thickness bias, which indicates that the thin cirrus clouds are realistically depicted during the model simulation.

Abstract

In this study, the ability of a new drought metric based on thermal infrared remote sensing imagery to provide early warning of an elevated risk for drought intensification is assessed. This new metric, called the rapid change index (RCI), is designed to highlight areas undergoing rapid changes in moisture stress as inferred from weekly changes in the evaporative stress index (ESI) generated using the Atmosphere–Land Exchange Inverse (ALEXI) surface energy balance model. Two case study analyses across the central United States revealed that the initial appearance of negative RCI values indicative of rapid increases in moisture stress preceded the introduction of severe-to-exceptional drought in the U.S. Drought Monitor (USDM) by more than 4 weeks. Using data from 2000 to 2012, the probability of USDM intensification of at least one, two, or three categories over different time periods was computed as a function of the RCI magnitude. Compared to baseline probabilities, the RCI-derived probabilities often indicate a much higher risk for drought development that increases greatly as the RCI becomes more negative. When the RCI is strongly negative, many areas are characterized by intensification probabilities that are several times higher than the baseline climatology. The highest probabilities encompass much of the central and eastern United States, with the greatest increase over climatology within regions most susceptible to rapid drought development. These results show that the RCI provides useful drought early warning capabilities that could be used to alert stakeholders of an increased risk for drought development over subseasonal time scales.