We have a long history of usage of total lightning data via the North Alabama Lightning Mapping Array (LMA) data here at the National Weather Service office in Huntsville, AL. LMA data began flowing here way back in the spring of 2003. There have been minor interruptions of data at times, mainly during and shortly after the implementation of AWIPS II, but total lightning data have been an integral, consistent part of operations here for over 10 years. These data have been used most often for detecting the initiation of electrical activity in developing convection. This is important because studies show that intra-cloud lightning often precedes cloud-to-ground lightning by about 5 to 10 minutes. Thus, total lightning data can serve as an early warning signal of the more dangerous cloud-to-ground component. We’ve also used the data to help identify thunderstorms that may experience rapid intensification, since total lightning activity is directly related to strengthening updrafts. I’ve even posted about an event in March 2012 where I used the LMA data as supplemental evidence that helped prompt a severe thunderstorm warning. This past Sunday evening (August 10th) I had the opportunity to use the data in a unique way (at least for me)…to help with a flash flood warning decision.
The first image below is a loop of KHTX WSR-88D 0.5 Reflectivity from this afternoon. Notice the cell that developed and persisted over northwestern portions of Morgan County. This cell developed directly along the Tennessee River and over the city of Decatur, AL.
The cell was producing heavy rainfall, and the other operational forecaster and I were watching it closely. One-hour rainfall amounts shortly after 2100 UTC were approaching 2 inches according to KHTX and nearby ground stations, which was near flash flood guidance for basins in this area. Of course, we were also dealing with data latency from these various sources, which is generally anywhere from about 5 to 20 minutes or more depending on the source. In a flash flooding situation, just as any other warning situation, things can evolve quickly and data updates as fast as possible are desired.
Perhaps most concerning however, was the fact that this cell was back-building and showing signs of little movement during the period, while some of this rain was falling over the city of Decatur. True, Decatur is a relatively small city, but still has sufficient urban land cover, and is bordered to the north and east by terrain that slopes gently toward the Tennessee River. So, drainage of water can be slow in the city, especially once adjacent backwaters and wetlands associated with the Tennessee River fill with water. While considering a flash flood warning, I still wanted some idea of the potential longevity of the cell over the Decatur area. The area could have handled this much rainfall if the cell dissipated and/or moved off as most others were prone to do in the low shear environment that day. However, looking at the LMA data really helped with my decision. Beginning at approximately 2104 UTC, source (image 2) and flash data (not shown) from the LMA showed the beginning of an enormous increase in total lightning activity with this cell. Also, the increase was taking place directly over the city of Decatur.
This trend in total lightning continued over the next several minutes. With the knowledge that this cell was likely undergoing intensification and moisture-laden updrafts were strengthening directly over the city of Decatur, I decided to issue the flash flood warning, which was officially disseminated at 2115 UTC. We received the first reports of flash flooding at 2145 UTC. The next image below shows the location of the warning issuance.
The total lightning data in this case served as a very valuable severe weather application tool. By providing the warning forecaster with knowledge of the location and likelihood of future deep convection, a flash flood warning was issued in a more timely and effective manner than would have been possible without these data. When used in conjunction with other information and applied properly, these types of data can help to save lives and property.