The composite structure of the Madden-Julian Oscillation (MJO) has long been known to feature pronounced Rossby gyres in the subtropical upper troposphere, whose existence can be interpreted as the forced response to convective heating anomalies in the presence of a subtropical westerly jet. Here we inquire as to whether these forced gyre circulations have any subsequent effects on divergence patterns in the tropics. A nonlinear spherical shallow water model is used to investigate how the introduction of different background jet profiles affects the model’s steady-state response to an imposed MJO-like thermal forcing. Results show that a stronger jet leads to a stronger Kelvin-mode response in the tropics up to a critical jet speed, along with stronger divergence anomalies in the vicinity of the forcing. To understand this behavior, additional calculations are performed in which a localized vorticity forcing is imposed in the extratropics, without any thermal forcing in the tropics. The response is once again seen to include pronounced equatorial Kelvin waves, provided the jet is of sufficient amplitude. A detailed analysis of the vorticity budget reveals that the zonal-mean zonal wind shear plays a key role in amplifying the Kelvin-mode divergent winds near the equator. These results help to explain why the MJO tends to be strongest during boreal winter when the Indo-Pacific jet is typically at its strongest.

Boreal-wintertime hindcasts in the Unified Forecast System with the tropics nudged toward reanalysis improve United States (U.S.) West Coast precipitation forecasts at Weeks 3-4 lead times when compared to those without nudging. To diagnose the origin of these improvements, a multivariate k-means clustering method is used to group hindcasts into subsets by their initial conditions. One cluster characterized by an initially strong Aleutian Low demonstrates larger improvements at Weeks 3-4 with nudging compared to others. The greater improvements with nudging for this cluster are related to the model error in simulating the interaction between the Aleutian Low and the teleconnection patterns associated with the Madden-Julian oscillation (MJO) and El Niño-Southern Oscillation (ENSO). Improving forecasts of tropical intraseasonal precipitation, especially during early MJO phases under non-cold ENSO, may be important for producing better Weeks 3-4 precipitation forecasts for the U.S. West Coast.

Weather forecasts made with imperfect models contain flow- and state-dependent errors. Data assimilation (DA) partially corrects these errors with new information from observations. As such, the corrections, or “analysis increments’, produced by the DA process embed information about model errors. An attempt is made here to extract that information to improve numerical weather prediction. Neural networks (NNs) are trained to predict corrections to the systematic error in the NOAA’s FV3-GFS model based on a large set of analysis increments. A simple NN focusing on an atmospheric column significantly improves the estimated model error correction relative to a linear baseline. Leveraging large-scale horizontal flow conditions using a convolutional NN, when compared to the simple column-oriented NN, does not improve skill in correcting model error. The sensitivity of model error correction to forecast inputs is highly localized by vertical level and by meteorological variable, and the error characteristics vary across vertical levels. Once trained, the NNs are used to apply an online correction to the forecast during model integration. Improvements are evaluated both within a cycled DA system and across a collection of 10-day forecasts. It is found that applying state-dependent NN-predicted corrections to the model forecast improves the overall quality of DA and improves the 10-day forecast skill at all lead times.