AWIPS Tracking Point Meteogram ToolKen Sperow1,2, Mamoudou Ba1, and Chris Darden3

1NOAA/NWS, Office of Science and Technology, Meteorological Development Laboratory, Silver Spring, MD 20910 2Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

3NOAA/NWS, Weather Forecast Office Huntsville, Huntsville, AL 35805Kenneth.Sperow@noaa.gov, Mamoudou.Ba@noaa.gov, and Chris.Darden@noaa.gov

Introduction

With the launch of GOES-R forecasters will have higher temporal and spatial resolution satellite data and exciting new products at their disposal. Forecaster tools to analyze this constantly increasing volume of meteorological data are more important today then ever before. Simple tools that help the forecasters focus on signals within the data rather than being overwhelmed by the volume of data are crucial. The Meteorological Development Laboratory (MDL) within the National Weather Service (NWS) is developing such a tool for use within AWIPS Migration.

AWIPS Migration

Tracking an Event

Once the forecaster has identified that there is an event or feature of interest by visually inspecting meteorological data within CAVE they can “track” the feature by overlaying the “Sample Tool” within CAVE (Figures 4-11) . When initially loaded the “Sample Tool” displays a point that must be dragged to the feature (Figure 4). After moving the point a track appears (Figure 5). The forecaster then steps forward or backward in time and adjusts the position of the point to overlay the feature of interest (Figures 6, 7, 8).

Summary

There are many potential uses of this tool, including but not limited to assessing cloud top cooling rates for clusters of convection or analyzing total cloud lightning data from the GOES Lightning Mapper (GLM) to make assessments of storm strength and growth rates. This tool could also be utilized in conjunction with channel difference fields to analyze a variety of trends including the potential for aircraft icing, overshooting tops, and fog and stratus development. A good example of trends of channel differences between water vapor and IR is the large positive differences seen in Figure 14 from 10:30 to 12:00 GMT which may indicate overshooting tops. Note the inversion of the trends and the subsequent collapse of convection (Figure 13).

The current prototype uses a simple point sampling approach, however, additional capabilities such as averaging or calculating the minimum or maximum value over an area has the potential to make the tool more useful. Additionally, the inclusion of tracking via non linear extrapolation might be beneficial in certain scenarios.

The Advanced Weather Interactive Processing System (AWIPS) was fielded to WFOs in the late 1990’s. AWIPS provides the information processing, display, and telecommunications system that integrates meteorological and hydrologic data, enabling our forecasters to prepare and issue timely, accurate weather forecasts and warnings. AWIPS Migration also known as AWIPS II is a large project currently under way within the NWS to replace the infrastructure within AWIPS so as to make it more open and flexible. Figures 1, 2, and 3 show different satellite images displayed within the Common AWIPS Visualization Environment (CAVE), the replacement for D-2D.

Figure 1: IR shown in CAVE.

Figure 2: Visible satellite shown in CAVE.

Figure 4: Sample Tool “Drag me to feature” mode.

Figure 5: Track appears after dragging point.

Figure 6: Zooming and adjusting to point of interest.

Figure 7: Stepping forward and adjusting position.

Meteogram

After the feature of interest has been tracked the forecaster has the ability to plot a meteogram (time-series) of the layers of interest. By selecting the layer(s) of interest and clicking “Plot Meteogram” (Figure 10) a meteogram is displayed (Figure 11). The meteogram is resizable and clicking any point within the meteogram displays cross lines making it easier to see the value of the data at a particular time (Figure 12). The interface also allows the forecaster to display more than one time series within the same meteogram (Figures 13, 14). Currently, data values are determined by calculating the value of the image at the selected point.

Figure 8: Another position adjustment.

Figure 9: Confirming event placement at the next time step.

Figure 10: Select the layer to display in the meteogram.

Figure 3: Water vapor satellite shown in CAVE.

Figure 12: Meteogram enlarged with cross lines to allow for closer inspection.

Figure 11: Meteogram showing value of the underlying layer’s data parameter changing in time and space.

Figure 13: Three series with different data axes.

Figure 14: Close up of time trend of three different fields.