Ensemble-based data assimilation of radar observations across inner-core regions of tropical cyclones (TCs) in tandem with satellite all-sky infrared radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all-sky microwave radiances using Hurricane Harvey (2017) as an example. Assimilating GPM constellation all-sky microwave radiances in addition to GOES-16 all-sky infrared radiances reduces the forecast errors in the TC track, rapid intensification, and peak intensity compared to assimilating all-sky infrared radiances alone, including a 24-hour increase in forecast lead-time for rapid intensification. Assimilating all-sky microwave radiances also improves Harvey’s hydrometeor fields, which leads to improved forecasts of rainfall after Harvey’s landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC-associated hazards in the future.

Atmospheric dust is a more extreme modifier of weather and climate on Mars than water vapor is on Earth. Global dust storms enshroud Mars in a veil of dust for months and have major implications for past and present climate, geologic history, habitability, and exploration. Yet their mysterious origins mean we remain unable to realistically simulate or predict them. In this White Paper, we find that key Knowledge Gaps are: A. how dust is lifted; B. constraints on near-surface winds and boundary-layer processes; C. the distribution of mobile surface dust; and D. the key processes and feedbacks by which dust storms begin and evolve. To make progress in the next decade, we make four Recommendations in order of priority: #1. Properly accommodate a minimum payload of meteorological and aeolian sensors on future Mars surface missions; #2. Continue orbital monitoring of the evolving surface dust distribution; #3. Expand orbital measurements to include winds and full diurnal coverage; and #4. Continue orbital monitoring and add surface measurements of aerosols during dust storms.

Data assimilation (DA) has been applied to estimate the time-mean state such as annual mean surface temperature for paleoclimate reconstruction. There are two types of DA for this purpose: online-DA and offline-DA. The online-DA estimates the time-mean states and the initial conditions for the next DA cycles while the offline-DA only estimates the former. If there is sufficiently long predictability in the system of interest compared to the temporal resolution of the observations, online-DA is expected to outperform offline-DA by utilizing information in the initial conditions. However, previous studies failed to show the superiority of online-DA when time-averaged observations are assimilated, and the reason has not been investigated thoroughly. This study compares online-DA and offline-DA and investigates the relation to the predictability using an intermediate complexity general circulation model with perfect-model observing system simulation experiments. The result shows that the online-DA outperforms offline-DA when the length of predictability is longer than the averaging time of the observations. We also found that the longer the predictability, the more skillful the online-DA. Here, the ocean plays a crucial role in extending predictability, which helps online-DA to outperform offline-DA. Interestingly, the observations of near-surface air temperature over land are found to be highly valuable to update the ocean variables in the analysis steps, suggesting the importance to use cross-domain covariance information between the atmosphere and the ocean when online-DA is applied to reconstruct paleoclimate.