Studying deep convective clouds requires the use of available observation platforms with high temporal and spatial resolution, as well as other non-remote sensing meteorological data (i.e., numerical weather prediction model output, conventional observations, etc.). Such data are often at different temporal and spatial resolutions, and consequently, there exists the need to fuse these different meteorological datasets into a single framework. This paper introduces a methodology to identify and track convective cloud objects from convective cloud infancy [as few as three Geostationary Operational Environmental Satellite (GOES) infrared (IR) pixels] into the mature phase (hundreds of GOES IR pixels) using only geostationary imager IR window observations for the purpose of monitoring the initial growth of convective clouds. The object tracking system described within builds upon the Warning Decision Support System-Integrated Information (WDSS-II) object tracking capabilities. The system uses an IR-window-based field as input to WDSS-II for cloud object identification and tracking and a Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin (UW-CIMSS)-developed postprocessing algorithm to combine WDSS-II cloud object output. The final output of the system is used to fuse multiple meteorological datasets into a single cloud object framework. The object tracking system performance analysis shows improved object tracking performance with both increased temporal resolution of the geostationary data and increased cloud object size. The system output is demonstrated as an effective means for fusing a variety of meteorological data including raw satellite observations, satellite algorithm output, radar observations, and derived output, numerical weather prediction model output, and lightning detection data for studying the initial growth of deep convective clouds and temporal trends of such data.
In this study, atmospheric analyses obtained through assimilation of temperature, water vapor, and wind profiles from a potential network of ground-based remote sensing boundary layer profiling instruments were used to generate short-range ensemble forecasts for each assimilation experiment performed in Part I. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). Overall, the results show that the most accurate forecasts were achieved when mass (temperature and humidity profiles from the RAM, MWR, and/or AERI) and momentum (wind profiles from the DWL) observations were assimilated simultaneously, which is consistent with the main conclusion from Part I. For instance, the improved wind and moisture analyses obtained through assimilation of these observations contributed to more accurate forecasts of moisture flux convergence and the intensity and location of accumulated precipitation (ACPC) due to improved dynamical forcing and mesoscale boundary layer thermodynamic structure. An object-based verification tool was also used to assess the skill of the ACPC forecasts. Overall, total interest values for ACPC matched objects, along with traditional forecast skill statistics like the equitable threat score and critical success index, were most improved in the multisensor assimilation cases.
In this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7-8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.
The evolution of an undular bore and its associated wind shift, spawned by the passage of a shallow surface cold front over the Southern Great Plains of the United States, is examined using surface and remote sensing observations along with output from a high-resolution numerical model simulation. Observations show that a separation between the wind shift and thermodynamic properties of the front was induced by the formation of a bore over south-central Kansas around 0200 UTC 29 November 2006. By the time the front-bore complex passed through Lamont, Oklahoma, approximately 4 h later, the bore had reached its maximum intensity and its associated wind shift preceded the trailing baroclinic zone by 20 min. Within several hours the bore decayed and a cold frontal passage, characterized by a wind shift coincident with thermodynamic properties was observed at Okmulgee, Oklahoma. Thus, a substantial transformation in both the structural and dynamical characteristics of the bore as well as its relationship to the parent surface front occurred during a short period of time.The details of this evolution are examined using output from a finescale numerical simulation, performed using the Weather Research and Forecasting (WRF) model. Analysis of the output reveals that as the bore advanced southeastward it moved into a region with a weaker surface stable layer. Consequently, the wave duct that had supported its maintenance steadily weakened resulting in dissipation of the bore. This circumstance led to a merger of the surface temperature and moisture boundaries with the orphaned wind shift, resulting in the cold frontal passage observed at Okmulgee.
The University of Wisconsin Convective Initiation (UWCI) algorithm utilizes geostationary IR satellite data to compute cloud-top cooling (UW-CTC) rates and assign CI nowcasts to vertically growing clouds. This study is motivated by National Weather Service (NWS) forecaster reviews of the algorithm output, which hypothesized that more intense cloud-top cooling corresponds to more vigorous short-term (0–60 min) convective development. An objective validation of UW-CTC rates using a satellite-based object-tracking methodology is presented, along with a prognostic evaluation of such cloud-top cooling rates for use in forecasting the growth and development of deep convection. In general, both a cloud object’s instantaneous and maximum cooling rate(s) are shown to be useful prognostic tools in predicting future radar intensification. UW-CTC rates are shown to be most skillful in detecting convective clouds that achieved intense radar signatures. The UW-CTC rate lead time ahead of the various radar fields is also shown, along with an illustration of the benefit of UW-CTC rates in operational forecasting. The results of this study suggest that convective clouds with the strongest UW-CTC rates are more likely to achieve significant near-term (0–60 min) radar signatures in such fields as composite reflectivity, vertically integrated liquid (VIL), and maximum estimated size of hail (MESH) compared to clouds that exhibit only weak UW-CTC rates.
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