The ability of four regional climate models (RCMs) to represent the Indian monsoon was verified in a consistent framework for the period 1981–2000 using the 45-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as lateral boundary forcing data. During the monsoon period, the RCMs are able to capture the spatial distribution of precipitation with a maximum over the central and west coast of India, but with important biases at the regional scale on the east coast of India in Bangladesh and Myanmar. Most models are too warm in the north of India compared to the observations. This has an impact on the simulated mean sea level pressure from the RCMs, being in general too low compared to ERA-40. Those biases perturb the land–sea temperature and pressure contrasts that drive the monsoon dynamics and, as a consequence, lead to an overestimation of wind speed, especially over the sea. The timing of the monsoon onset of the RCMs is in good agreement with the one obtained from observationally based gridded datasets, while the monsoon withdrawal is less well simulated. A Hovmöller diagram representation of the mean annual cycle of precipitation reveals that the meridional motion of the precipitation simulated by the RCMs is comparable to the one observed, but the precipitation amounts and the regional distribution differ substantially between the four RCMs. In summary, the spread at the regional scale between the RCMs indicates that important feedbacks and processes are poorly, or not, taken into account in the state-of-the-art regional climate models.

The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.

Soil moisture can influence precipitation through a feedback loop with land surface evapotranspiration. A series of numerical simulations, including soil moisture sensitivity experiments, have been performed for the Indian summer monsoon season (ISM). The simulations were carried out with the nonhydrostatic regional climate model Consortium for Small-Scale Modeling (COSMO) in climate mode (COSMO-CLM), driven by lateral boundary conditions derived from the ECMWF Interim reanalysis (ERA-Interim). Positive as well as negative feedback processes through local and remote effects are shown to be important. The regional moisture budget studies have exposed that changes in precipitable water and changes in precipitation efficiency vary in importance, in time, and in space in the simulations for India. Overall, the results show that the premonsoonal soil moisture has a significant influence on the monsoonal precipitation, and thus confirmed that modeling of soil moisture is essential for reliable simulation and forecasting of the ISM.

A multimodel evaluation of subseasonal‐to‐seasonal (S2S) hindcast skill of atmospheric rivers (ARs) out to 4‐week lead over the western United States is presented for three operational hindcast systems: European Centre for Medium‐Range Weather Forecasts (ECMWF; Europe), National Centers for Environmental Prediction (NCEP; U.S.), and Environment and Canada Climate Change (ECCC; Canada). Ensemble mean biases and Brier Skill Scores are examined for no, moderate, and high levels of AR activity (0, 1–2, and 3–7 AR days/week, respectively). All hindcast systems are more skillful in predicting no and high AR activity relative to moderate activity. There are isolated regions of skill at week‐3 over 150–125°W, 25–35°N for the no and high AR activity levels, with larger magnitude and spatial extent of the skill in ECMWF and ECCC compared to NCEP. The spatial pattern of this skill suggests that for high AR activity, a southwest‐to‐northeast orientation is more predictable at subseasonal lead times than other orientations, and for no AR activity, more skill exists in the subtropical North Pacific, upstream of central and southern California. AR hindcast skill along the western U.S. is most strongly increased in hindcasts initialized during Madden‐Julian Oscillation (MJO) Phases 1 and 8, and hindcast skill is substantially decreased over California in hindcasts initialized during MJO Phase 4. Skill modulations in the ECMWF hindcast system conditioned on El Niño‐Southern Oscillation phase are weaker than those conditioned on particular MJO phases. This work provides hindcast skill benchmarks and uncertainty quantification for experimental real‐time forecasts of AR activity during winters 2019–2021 as part of the S2S Prediction Project Real‐time Pilot Initiative in collaboration with the California Department of Water Resources.