Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of extreme winter weather in California, including ARs, SAWs and temperature extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1-4 lead. This methodology (i) uses dynamical model information considered most reliable, i.e., planetary/synoptic-scale atmospheric circulation, (ii) filters for dynamical model error/uncertainty at longer lead times, and (iii) increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support.

Accurate global river discharge estimation is crucial for advancing our scientific understanding of the global water cycle and supporting various downstream applications. In recent years, data-driven machine learning models, particularly the Long Short-Term Memory (LSTM) model, have shown significant promise in estimating discharge. Despite this, the applicability of LSTM models for global river discharge estimation remains largely unexplored. In this study, we diverge from the conventional basin-lumped LSTM modeling in limited basins. For the first time, we apply an LSTM on a global 0.25° grid, coupling it with a river routing model to estimate river discharge for every river reach worldwide. We rigorously evaluate the performance over 5332 evaluation gauges globally for the period 2000-2020, separate from the training basins and period. The grid-scale LSTM model effectively captures the rainfall-runoff behavior, reproducing global river discharge with high accuracy and achieving a median Kling-Gupta Efficiency (KGE) of 0.563. It outperforms an extensively bias-corrected and calibrated benchmark simulation based on the Variable Infiltration Capacity (VIC) model, which achieved a median KGE of 0.466. Using the global grid-scale LSTM model, we develop an improved global reach-level daily discharge dataset spanning 1980 to 2020, named GRADES-hydroDL. This dataset is anticipated to be useful for a myriad of applications, including providing prior information for the Surface Water and Ocean Topography (SWOT) satellite mission. The dataset is openly available via Globus.

The elevation of the mountain rain-snow transition is critical for short-term hazard forecasting and longer-term water supply considerations. Despite the transition’s importance, direct in-situ observations are rare. Here we present two new methods that utilize “anomalous” snow observations to detect rainfall during rain-on-snow: (1) a mass fluctuation at snow pillow sites, and (2) inflated remotely sensed snow grain sizes. Using auxiliary data, we show snow pillows respond to rain-on-snow with distinct perturbations that appear as pulses, collapses and declines within the snow water equivalent. We use these responses to identify mountain-scale rain-snow transitions across California’s Sierra Nevada. We also show how a threshold approach (>200 mm) for remotely sensed snow grain size can identify rain-on-snow as snow grain sizes artificially inflate due to a liquid water film. While the methods are not predictive, if paired retroactively with hydrometeorological models, these new methods have the potential to improve predictive streamflow capabilities.

We discuss improving forecasts of winds in the lower stratosphere using machine learning to post-process the output of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System. We post-process global three-dimensional predictions, and demonstrate distilling the analog ensemble (AnEn) method into a deep neural network which reduces post-processing latency to near zero maintaining increased forecast skill. This approach reduces the error with respect to ECMWF high-resolution deterministic prediction between 2-15% for wind speed and 15-25% for direction, and is on par with ECMWF ensemble (ENS) forecast skill to hour 60. Verifying with Loon data from stratospheric balloons, AnEn has 20% lower error than ENS for wind speed and 15% for wind direction, despite significantly lower real-time computational cost to ENS. Similar performance patterns are reported for probabilistic predictions, with larger improvements of AnEn with respect to ENS. We also demonstrate that AnEn generates a calibrated probabilistic forecast.

Traditional post-processing methods have relied on point-based applications that are unable to capture complex spatial precipitation error patterns. With novel ML methods using convolution to more effectively identify and reduce spatial biases, we propose a modified U-Net convolutional neural network (CNN) to post-process daily accumulated precipitation over the US west coast. For training, we leverage 34 years of deterministic Western Weather Research and Forecasting (West-WRF) reforecasts. On an unseen 4-year data set, the trained CNN yields a 12.9-15.9% reduction in root mean-square error (RMSE) over West-WRF for lead times of 1-4 days. Compared to an adapted Model Output Statistics baseline, the CNN reduced RMSE by 7.4-8.9% for all events. Effectively, the CNN adds more than a day of predictive skill when compared to West-WRF. The CNN outperforms the other methods also for the prediction of extreme events, highlighting a promising path forward for improving precipitation forecasts.

Satellites provide the primary dataset for monitoring the earth system and constraining analyses in numerical models. A challenge for utilizing satellite radiances is the estimation of their biases. High-accuracy non-radiance data are typically employed to anchor radiance bias corrections. This study provides the first assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program that samples ARs over the Northeast Pacific on the radiance assimilation using the Global Forecast System (GFS) and the Global Data Assimilation System. Including this dropsonde dataset has provided better anchoring for bias corrections and improved model background, leading to an increase of ~5-10% in the amount of assimilated microwave radiance in the lower/middle troposphere over the Northeast Pacific and North America. The impact on tropospheric infrared radiance is small but also beneficial. This result points to the usefulness of dropsondes, along with other conventional data, in the assimilation of satellite radiance.