Early warning systems element of daily variation. Correct weather prediction depends heavily on the availability of reliable data, so any gaps in coverage, inaccuracies or errors in representing initial atmospheric conditions, or outdated observational infrastructure can hinder precise prediction. The challenge is that measuring water vapor in the atmosphere has always been manual, time-consuming and expensive – until now. Continuous and autonomous water vapor monitoring Advances in remote sensing technologies empower the meteorological industry to cost-effectively enhance severe weather forecasting and climate modeling capabilities with advanced, real-time water vapor profiles. The Vaisala DA10 differential absorption lidar (DIAL) is the industry’s first continuous and autonomous water vapor monitoring solution created for observation networks. It uses dual wavelengths to measure the water vapor mixing ratio in the boundary layer. While globally coordinated upper-air observations provide an overall picture of humidity patterns, the DA10 continuously measures water vapor in the boundary layer, in any location, under any conditions to deliver previously unavailable research-grade data suitable for NWP modeling. Essentially a ceilometer and a water vapor profiler combined, the device creates two different profiles – one independent of water vapor and one dependent on water vapor – to determine the actual water vapor mixing ratio within the boundary layer. Combining the real water vapor mixing ratio profile within the boundary layer and the uncertainty profile for that measurement provides information readily available for assimilation. Designed to support nowcasting, forecasting, climate modeling and atmospheric research, the DA10 provides atmospheric profiling data, including information on water vapor mixing ratio profiles (g/kg), uncertainty for the water vapor mixing ratio (g/kg) and attenuated backscatter profiles (from the surface up to 18km); and atmospheric parameter data, including information on cloud base heights (up to five layers), cloud and penetration depth, precipitation/fog detection, sky condition and surface pressure, temperature and humidity. The resulting information equips meteorological decision makers with time and height plots illustrating the changes in water vapor in the atmosphere and within the boundary layer. Unlike traditional methods that rely on sporadic weather balloon launches, continuous humidity monitoring ensures meteorologists can access up-to-the-minute information The resulting data provides, for example, time and height plots where you can see the changes in water vapor both in the atmosphere and the boundary layer and integrate it into numerical weather prediction models at all hours, minimizing the lead time for issuing weather warnings. Organizations can improve geographical data coverage by establishing or enhancing the forecasting capabilities of operational networks with real-time water vapor profiling. Knowing the amount of water vapor in the atmosphere helps meteorologists predict the likelihood and intensity of precipitation and issue weather warnings and advisories, especially in the case of heavy rainfall events and severe storms. Consider the UK Met Office and Deutscher Wetterdienst (DWD) as cases proving that the DA10 can help to improve NWP modeling and nowcasting capabilities. The one-month UK Met Office measurement trial saw measurement error close to the 5% OSCAR breakthrough requirement and minimal overall bias, 0.1g/kg, with a correlation of 0.93 with the radiosonde measurement. The agreement between the radiosonde and the DA10 was better than the model versus the radiosonde, cementing that DA10 data should improve the model when it is assimilated. For DWD’s 2021 Field Experiment on Submesoscale Spatiotemporal Variability in Lindenberg (FESSTVaL) campaign, the German weather service DWD compared DA10 and radiosonde measurements. The pilot results showed that the DA10 successfully covered water vapor structure and evolution and captured mixing ratio profiles/gradients. Fueling tomorrow’s severe weather forecasting Afternoon thunderstorms and flash floods develop rapidly and are hard to predict With the frequency and severity of extreme weather events surging in the face of our changing climate, strengthening early warning systems through better observations and forecasts will only grow in importance. Thankfully, continuous high-resolution humidity profiling provides the data needed to monitor water vapor profiles in real time, enabling more precise and earlier alerts for severe storms. By capturing previously unavailable variability in low-level moisture, innovative technologies like differential absorption lidar fill critical gaps in existing observation networks, unlocking the potential to significantly improve the numerical weather prediction models forecasting thunderstorms and heavy rainfall. Solutions like the Vaisala DA10 are indispensable in the arsenal of meteorologists and forecasters tasked with keeping our communities safe in a changing climate. The future of adequate early warnings relies on transformative technologies and collaborative efforts to effectively prepare and respond to potentially life-threatening weather events. 50 • www.meteorologicaltechnologyinternational.com • January 2024