|Title||Atmospheric river reconnaissance observation impact in the Navy global forecast system|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Stone R.E, Reynolds C.A, Doyle J.D, Langland R.H, Baker N.L, Lavers D.A, Ralph FM|
|Type of Article||Article|
|Keywords||adjoint sensitivity; assimilation; data; dropsondes; ensemble transform; Field experiments; forecasting; formulation; Meteorology & Atmospheric Sciences; navdas-ar; Numerical weather prediction; precipitation; tests|
Atmospheric rivers, often associated with impactful weather along the west coast of North America, can be a challenge to forecast even on short time scales. This is attributed, at least in part, to the scarcity of eastern Pacific in situ observations. We examine the impact of assimilating dropsonde observations collected during the Atmospheric River (AR) Reconnaissance 2018 field program on the Navy Global Environmental Model (NAVGEM) analyses and forecasts. We compare NAVGEM's representation of the ARs to the observations, and examine whether the observation-background difference statistics are similar to the observation error variance specified in the data assimilation system. Forecast sensitivity observation impact is determined for each dropsonde variable, and compared to the impacts of the North American radiosonde network. We find that the reconnaissance soundings have significant beneficial impact, with per observation impact more than double that of the North American radiosonde network. Temperature and wind observations have larger total and per observation impact than moisture observations. In our experiment, the 24-h global forecast error reduction from the reconnaissance soundings can be comparable to the reduction from the North American radiosonde network for the field program dates that include at least two flights.