Much of the errors of Atmospheric Motion Vectors (AMV) may be a consequence of algorithms not incorporating dynamical information. A physics-informed, artificial intelligence algorithm was developed that corrects errors of moisture tracking AMV (from the movement of water vapor) using Numerical Weather Prediction (NWP) fields. The University of Arizona algorithm (UA) uses a variational method as a first step (fsUA), the second step then filters the first-stage AMVs using a Random Forest model that learns the error correction from NWP fields. The UA algorithm is compared with a traditional image feature tracking algorithm (JPL) using a global nature run as the “ground truth”. Experiments use global all sky humidity fields at 500 hPa and 850 hPa, and for January 1-3, 2006, and July 1-3, 2006. UA outputs AMVs with Root Mean Square Vector Differences (RMSVDs) of 2 m/s for the tropics and ~ 2 - 3 m/s for mid-latitudes and the poles, whereas JPL outputs much higher RMSVDs of ~ 3 m/s for the tropics and ~ 3 - 9 m/s for the mid-latitudes and poles. Although the algorithm fsUA produces approximately the same global RMSVDs as the JPL algorithm, fsUA has a higher resolution since it outputs an AMV per pixel, whereas the JPL algorithm uses a target box that effectively smooths the vectors. Furthermore, UA’s RMSVDs are lower than the intrinsic error (calculated from the differences between two reanalysis datasets). Even for error-prone regions with low moisture gradients and where winds are oriented along moisture isolines, UA’s absolute speed difference with “truth” stays within ~ 3 m/s.

Mesoscale convective systems (MCSs) in the tropics play an integral role in the water cycle, are associated with local hazardous weather conditions, and have significant remote impacts on the midlatitude jet stream. Although it is known that MCSs occur in relatively moist environments, it is unclear how far in advance favorable ingredients (lift, instability, and moisture) in the mesoscale environment precede MCS formation. In this study, an automated MCS tracking algorithm and global reanalyses are used to examine the pre-MCS environment for 3295 MCSs that occurred in the tropics in a 3-month period. Results showed that increased water vapor and mesoscale ascent implied by low-level convergence and upper-level divergence preceded MCS formation by up to 24 h. Regional variations in pre-MCS environment conditions were apparent and are discussed. Future work will study to what extent these moisture and wind anomalies can be used to predict MCS formation.

The three-dimensional (3D) structure of the global horizontal wind field remains largely unobserved . Atmospheric Motion Vectors (AMVs) based on cloud tracking have been used since the 1960s to fill some of the gaps in global wind fields observation (Eyre et al., 2020). In this type of AMVs, features in clouds are tracked across sequential images. These cloud-tracking AMVs are derived from geostationary (GEO) satellite and Low Earth Orbiting (LEO) satellite measurements (Key et al., 2003;.However, all these cloud-tracking AMVs face the same problem: clouds in general are sparse in the 850-500 hPa layers, leaving most of the vertical structure in the middle troposphere unobserved . Furthermore, each cloud-tracking AMV derived from an image sequence is assigned to a single level of cloud top at a given horizontal coordinate thus, dynamic quantities such as wind shear, vorticity, and divergence cannot be obtained. Finally, in the case of cloud-tracking winds, height assignment forms the largest source of error (Salonen et al., 2015).An alternative is AMVs retrieved from radiances of water vapor bands (Velden et al., 1997(Velden et al., , 2005. Indeed, these AMVs are widely used (e.g., http://tropic.ssec.wisc.edu/misc/winds/info.html). However, these water vapor AMVs have poor vertical resolution, since they are derived from instruments with limited water vapor bands. Other relevant observations to our work are the wind speeds derived by a wind lidar carried by a LEO satellite (Aeolus) that calculates the wind speed in its line of sight (LOS) with high vertical resolution (e.g., 0.25 km near surface; Stoffelen et al., 2020). Assimilation of Aeolus winds in numerical weather prediction models causes statistically significant increases in forecast skill (Rennie et al., 2021). Recent work shows that the LOS wind component derived by Aeolus is around 5 m s −1 for Rayleigh scattering and 4 m s −1 for Mie scattering (these values

The weather and climate over the coastal regions have received increasing attention because of substantial population growth, the rising sea level, and extreme weather. Satellite remote sensing provides global precipitation estimates (including coastal land/ocean). While these datasets have been extensively evaluated over land, they have rarely been assessed over coastal ocean. As precipitation radars cover both coastal land and ocean, we used the Multi-Radar/Multi-Sensor System (MRMS) gauge-corrected precipitation product from 2018 to 2020 to evaluate three widely used satellite-based precipitation products over the U.S. coastal land versus the ocean (and the water over the Great Lakes). These products included the Integrated Multi-satellite Retrievals for GPM (IMERG), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center Morphing technique (CMORPH). The MRMS data showed a precipitation climatology difference between the coastal land and the ocean that was higher in the winter and lower in the summer and autumn. IMERG and CMORPH performed best over land and water, respectively, while PERSIANN was the most consistent in its performance over land versus water. Heavy precipitation was overestimated by the three products, with larger overestimates over water than over land. These results were not affected by the MRMS uncertainties due to the gauge correction or by the use of different versions.