Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS

September 26, 2024

A new article titled “Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS,” led by CW3E scientist Minghua Zheng and co-authored by Luca Delle Monache (CW3E), Xingren Wu (NOAA/NCEP/EMC), Brian Kawzenuk (CW3E), F. Martin Ralph (CW3E), Yanqiu Zhu (NASA/GSFC), Ryan Torn (U. Albany), Vijay Tallapragada (NOAA/NCEP/EMC), Zhenhai Zhang (CW3E), Keqin Wu (CW3E), and Jia Wang (CW3E) was recently published in the American Meteorological Society’s Journal of Atmospheric and Oceanic Technology. This study assesses the critical role of Atmospheric River (AR) Reconnaissance dropsonde data in improving the assimilation of satellite radiance within the Global Forecast System (GFS) at NCEP. 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 underscores the importance of data collected during AR Reconnaissance missions, which sample ARs over the northeastern Pacific, in improving satellite radiance assimilation for more accurate weather forecasting.

Satellites provide one of the largest datasets for monitoring and modeling the Earth system, but accurately estimating radiance biases poses significant challenges for numerical weather prediction (NWP) models. Non-radiance data, such as those collected by dropsondes, radiosondes, and radio occultation profiles from GPS satellites, serve as essential tools for anchoring radiance bias corrections. The study highlights how the integration of dropsonde data from Atmospheric River Reconnaissance missions enhances the GFS and Global Data Assimilation System (GDAS) by improving the model background and increasing the assimilation of microwave radiances by 5%–10% in the lower and mid-troposphere over the northeastern Pacific and North America (Fig. 1). Additionally, while the impact on tropospheric infrared radiance is small, it remains beneficial. However, the influence on radiance use in the stratosphere is minimal due to limited dropsonde data at higher altitudes.

These findings emphasize the importance of dropsonde observations, alongside other unbiased conventional data sources, in refining satellite radiance assimilation. This study reinforces the value of a diverse observational network in enhancing forecast accuracy and highlights the specific indirect effects of integrating dropsonde data into the data assimilation process within a cycled NWP system, in addition to their direct positive impacts on model analyses and forecast demonstrated in previous literature (e.g., Zheng et al. 2021; DeHaan et al. 2023).

Figure 1. Statistics of AMSU-A channel 1 radiance observations that successfully passed the QC over the Pacific and North America (PNA) region in GDAS for the Ctrl and Deny runs from 24 Jan to 18 Mar 2020. (a) Radiance counts in Ctrl (red dots) and Deny (blue dots); (b) percentage change of radiance count (green dots) in Ctrl w.r.t. Deny, and the 7-point running mean (dark green line); (c) STD (K) for OmB in Ctrl (red dots) and Deny (blue dots); (d) percent of STD change in Ctrl w.r.t. Deny; (e) abs. TBC (K) in Ctrl (red dots) and Deny (blue dots); (f) percentage change of abs. TBC in Ctrl w.r.t. Deny. Each dot represents data from a 6-h data assimilation window centered at 0000 or 1200 UTC. Magenta stars at the bottom of each panel denote 17 AR Recon IOPs during AR Recon 2020 field season. Ctrl assimilated dropsondes during the assimilation process while Deny removed dropsondes. This figure is modified from Figure 3 of Zheng et al. (2024).

Zheng, M., Delle Monache, L., Wu, X., Kawzenuk, B., Ralph, F. M., Zhu, Y., Torn, R., Tallapragada, V. S., Zhang, Z., Wu, K. and Wang, J., 2024. Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS. Journal of Atmospheric and Oceanic Technology, 49(9), 819-832. https://doi.org/10.1175/JTECH-D-23-0167.1

Zheng, M., Delle Monache, L., Cornuelle, B. D., Ralph, F. M., Tallapragada, V. S., Subramanian, A., Haase, J. S., Zhang, Z., Wu, X., Murphy, M. J. and Higgins, T. B., 2021. Improved forecast skill through the assimilation of dropsonde observations from the Atmospheric River Reconnaissance program. Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS. Journal of Geophysical Research: Atmospheres, 126(21), e2021JD034967. https://doi.org/10.1029/2021JD034967

DeHaan, L. L., Wilson, A. M., Kawzenuk, B., Zheng, M., Monache, L. D., Wu, X., Lavers, D. A., Ingleby, B., Tallapragada, V., Pappenberger, F. and Ralph, F. M., 2023. Impacts of Dropsonde Observations on Forecasts of Atmospheric Rivers and Associated Precipitation in the NCEP GFS and ECMWF IFS models. Weather and Forecasting, 38(12), 2397-2413. https://doi.org/10.1175/WAF-D-23-0025.1