An Assessment of Dropsonde Sampling Strategies for Atmospheric River Reconnaissance

January 31, 2024

A new article titled “An Assessment of Dropsonde Sampling Strategies for Atmospheric River Reconnaissance” By Minghua Zheng (CW3E), Ryan Torn (University at Albany), Luca Delle Monache (CW3E), James Doyle (Naval Research Laboratory), F. Martin Ralph (CW3E), Vijay Tallapragada (NOAA/NCEP/EMC), Christopher Davis (NCAR), Daniel Steinhoff (CW3E), Xingren Wu (NOAA/NCEP/EMC), Anna Wilson (CW3E), Caroline Papadopoulos (CW3E), and Patrick Mulrooney (CW3E) was recently published in the American Meteorological Society’s Monthly Weather Review. As part of CW3E’s 2019-2024 Strategic Plan to support Atmospheric River Research and Applications, CW3E seeks to enhance global AR monitoring through a transformative modernization of atmospheric measurements over the Pacific and in the western United States. In alignment with this goal, the study explores the impact of varying mission frequency, dropsonde spacing, and aircraft utilized during AR Reconnaissance (AR Recon) on the forecast skill of an AR-related heavy precipitation event that was sampled over a 6-day sequence of Intensive Observing Periods (IOPs) in 2021.

Throughout the 6-day IOP in late January of 2021, AR Recon aircraft sampled a series of ARs over the northeastern Pacific, resulting in heavy precipitation over coastal regions of California and the Sierra Nevada Mountains. Using these observations, data denial experiments were conducted with a regional modeling and data assimilation system to explore the impacts of different flight scenarios and dropsonde sampling strategies.

Results indicate that dropsondes significantly improve the representation of ARs in the model analyses and positively impact the forecast skill of ARs and quantitative precipitation forecasts (QPF), particularly for lead times > 1 day. Reduced mission frequency and reduced dropsonde horizontal spatial resolution both degrade forecast skill. On the other hand, experiments that assimilated only G-IV data and experiments that assimilated both G-IV and C-130 data show better forecast skill than experiments that only assimilated C-130 data, suggesting that the inclusion of two types of aircraft (G-IV and C-130s) is an effective strategy to enable the benefits of missions on a consecutive way.

This study suggests some promising guidance for flight planning in future operational AR Recon missions. The findings recommend that future operational AR Recon missions incorporate daily mission or consecutive back-to-back flights, maintain current dropsonde spacing, support high-resolution data transfer capacity on the C-130s, and utilize G-IV alongside C-130s.

Figure 1: Box plot of (a) the interest value, (b) the intersection area, and (c) the object size error for the coastal object validation in Figure 10 of Zheng et al. (2024). The box plots are calculated by combining all 19 lead times together, with the non-matched forecast object excluded in the corresponding lead time. The bottom and the top of each box represents the 25th percentile and the 75th percentile, respectively. The magenta line in the middle of the box is the median. The cyan asterisk is the mean value of each experiment. The magenta horizontal line is the median of each data. Panel (d) shows the p-value representing the degree of significance for the mean value differences between two experiments. The green shades in (d) correspond to that the 1st experiment in the parentheses has less errors for the three metrics while the red shades show the 2nd experiment has less errors. Bold values in the chart of (d) show two experiments are significantly different at the 80% confidence levels. TS stands for “temporal sampling”. This figure is modified from Figure 11 of Zheng et al. (2024).

Figure 2: Same as Figure 1 but for the SS (spatial sampling) experiments. This figure is modified from Figure 15 of Zheng et al. (2024).

Zheng, M., Torn, R., Delle Monache, L., Doyle, J., Ralph, F. M., Tallapragada, V., Davis, C., Steinhoff, D., Wu, X., Wilson, A. M., Papadopoulos, C., & Mulrooney, P. (2024). An Assessment of Dropsonde Sampling Strategies for Atmospheric River Reconnaissance. Monthly Weather Review (published online ahead of print 2024). https://doi.org/10.1175/MWR-D-23-0111.1