Treffer: Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill.

Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3‐cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection‐permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s−1, and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain‐driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado. Plain Language Summary: When we collect observations about the weather, we need to understand their characteristic uncertainties in order to use them effectively in weather prediction models. Uncrewed Aircraft Systems (UASs) show promise for filling in gaps in weather data, especially within the lowest 1.5 km of the atmosphere. However, since UAS are a relatively new sensing system, their errors and representativeness are not well characterized. To estimate their uncertainties (called observation error) we use a method that compares three independent estimates of the atmospheric state. We apply this method using UAS and radiosonde data collected during a 2018 field study in Colorado along with predictions from a high resolution numerical weather prediction model. We find that the observation error in UAS wind and humidity measurements is similar to that assumed for observations collected with commercial aircraft. However, our estimates of observation error of UAS temperature measurements were 50% less than that assumed for commercial aircraft temperature measurements. Using these new error estimates to assimilate UAS observations, we improve the accuracy of our weather analyses and forecasts for specific weather patterns in Colorado's San Luis Valley. Key Points: We apply a 3‐cornered hat diagnostic approach to Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment campaign data to estimate the Uncrewed Aircraft System observation errorUAS temperature observation error is smaller than that typically used to assimilate commercial aircraft observations in global NWP modelsSkill of analyses and forecasts generally improved when using smaller temperature observation error values [ABSTRACT FROM AUTHOR]

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