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The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite-16 (GOES-16) includes three water vapor channels (8, 9, and 10) that are specifically designed to monitor tropospheric water vapor at high, mid-, and low levels. This study investigates an effective approach for assimilating the all-sky brightness temperatures (BTs) derived from these three water vapor channels while considering the characteristics of interchannel observation-error correlations (IOECs). The NASA Unified Weather Research and Forecasting (NU-WRF) Model, alongside the NCEP Gridpoint Statistical Interpolation analysis system (GSI)-based three-dimensional ensemble-variational hybrid data assimilation system, is utilized. First, an optimal bias correction (BC) scheme and data assimilation (DA) configuration, previously developed for all-sky assimilation of GOES-16 channel 8, are confirmed to be effective for channels 9 and 10. Then, the IOECs with respect to the symmetric cloud proxy variable C ¯ among these three channels are analyzed. It is found that the IOECs display sigmoid function characteristics, being lower and roughly invariant with respect to C ¯ under clear-sky conditions and higher in cloudy conditions. Given the unique properties of IOECs, sensitivity experiments with various configurations for assimilating the all-sky BTs from the three water vapor channels are conducted with Hurricanes Laura (2020) and Ida (2021). The results indicate that a strategy combining the assimilation of all-sky BTs from one of the GOES-16 channels, especially channel 10, with clear-sky BTs from the other water vapor channels yields superior analysis and forecasts in most scenarios, thereby highlighting the importance of appropriately estimating and accounting for cloud-dependent IOECs when assimilating all-sky BTs from the infrared channels in operational DA systems. [ABSTRACT FROM AUTHOR]

This study investigates the offshore propagation of precipitation off the west coast of Sumatra on February 1 and 4, 2018, focusing on specific events to complement previous studies on seasonality. We use ERA5, MERRA–2, IMERG, Padang radar data, and cloud‐permitting scale simulations with WRF‐ARW V4.1.3 to understand the roles of the land breeze, gravity waves, and convective outflow in these two events. On February 1, convection organizes north of Padang and propagates approximately 100 km offshore. On February 4, a multicellular system organizes south of Padang and travels over 150 km offshore. Large‐scale environmental conditions reveal stronger westerly winds from the Indian Ocean on February 1 and strong northeasterly winds from the South China Sea on February 4. Both events occur in regions with high surface humidity and weak 900 mb winds along the coastline. WRF simulations show that on February 1, the sea breeze uniformly transitions to a land breeze, while on February 4, it forms chaotically. Convective outflow enhances offshore precipitation by interacting with the land breeze, creating additional convergence zones and intensifying convection. Gravity waves played a minor role, contributing to atmospheric instability. The results of this study conclude that offshore‐moving convection is primarily driven by the land breeze and convective outflow, with gravity waves playing a minor role. The land breeze initiates convection, while the convective outflow enhances and sustains offshore propagation by creating additional convergence zones and intensifying convection. Moisture advection precedes nocturnal propagation, increasing atmospheric instability and creating favorable conditions for nighttime offshore precipitation. Plain Language Summary: The Maritime Continent (Southeast Asia) receives some of the highest amounts of rainfall in the world. Predicting this rain, especially when it moves from land to sea, is tough because of complex weather patterns. This study looks at what causes rain to move offshore near Sumatra's west coast by focusing on two specific rainy days in February 2018. It is found that the rain starts inland in the evening and moves offshore during the night. This study used satellite and radar data as well as weather models to study large‐scale weather patterns and small‐scale details. They discovered that moisture moves with the wind in the afternoon, helping the rain shift offshore at night. The land breeze (wind from land to sea) and air currents from storms create rising air when they meet westerly winds, leading to more rain. Gravity waves, which are waves in the air caused by disturbances, also help trigger rain in one of the cases. Results from this study helps us understand how different weather factors work together to move rain offshore in the Maritime Continent. Key Points: Land breeze drives nocturnal precipitation offshore, generating convergence along its leading edge where it collides with ambient windConvective outflow enhances offshore precipitation by creating additional convergence zones and intensifying convectionMoisture advection and gravity waves increase atmospheric instability prior to nocturnal rainfall [ABSTRACT FROM AUTHOR]

Cold fog refers to a type of fog that forms when the temperature is below 0°C. It can be composed of liquid, ice, and mixed‐phase fog particles. Cold fog happens frequently over mountainous terrain in the cold season, but it is difficult to predict. Using observations from the Cold Fog Amongst Complex Terrain (CFACT) field campaign conducted in Heber Valley, Utah, in the western United States during January and February of 2022, this study investigates the meteorological conditions in the surface and boundary layers that support the formation of wintertime ephemeral cold fog in a local area of small‐scale mountain valleys. It is found that fog formation is susceptible to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated local clear skies, calm winds, and a stable boundary layer; (2) near‐surface inversion with saturation near the surface and strong moisture gradient in the boundary layer; (3) warm (above‐freezing) daytime air temperature with a large diurnal range, accompanied with warm soil temperatures during the daytime; (4) a period of increased turbulence kinetic energy (above 0.5 m2·s−2), followed by calm conditions throughout the fog's duration; and (5) supersaturation with respect to ice. Then, the field observations and identified supporting factors for fog formation were utilized to evaluate high‐resolution (˜400 m horizontal grid spacing) Weather Research and Forecasting (WRF) model simulations. Results show that the WRF model accurately simulates the mesoscale conditions facilitating cold‐fog formation but misses some critical surface and atmospheric boundary conditions. The overall results from this paper indicate that these identified factors that support fog formation are vital to accurately forecasting cold‐fog events. At the same time, they are also critical fields for the NWP model validation. [ABSTRACT FROM AUTHOR]

Drought monitoring is crucial for various sectors, and soil moisture data play a pivotal role, especially in agricultural contexts. This study focuses on the recent CYGNSS Level 3 soil moisture data derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS), notable for its wide coverage and rapid revisit times, yet underexplored in drought research. Spanning from 1 January 2018 to 31 December 2022, this research analyzed daily CYGNSS soil moisture data, comparing them with the ERA5, SMAP, and GLDAS-NOAH datasets. It was found that the average and standard deviation (std) of CYGNSS soil moisture exhibited spatial patterns largely similar to other datasets, although some regions showed discrepancies (std differences reached up to 0.05 in some regions). The correlation coefficients and RMSE values between CYGNSS and other datasets depended on climate and land cover types. Four drought indicators from different soil moisture datasets were compared with the improved monthly Standardized Precipitation Evapotranspiration Index (SPEI). The drought indicators based on CYGNSS data demonstrate the capacity to describe drought extent and intensity. The correlation coefficients between certain drought indicators obtained from CYGNSS and SPEI reached 0.27 for drought percentage and 0.16 for drought intensity. Further investigations with selected extreme drought cases revealed that the indicator from CYGNSS data is relatively weak, influenced by the selected regions, times, and drought indicators. The results of this study provide insights into the potential application of CYGNSS soil moisture data in drought monitoring, offering a foundation for future research and practical implementation with current and future improved products. [ABSTRACT FROM AUTHOR]

Vertical eddy diffusivity (VED) in the planetary boundary layer (PBL) has a significant impact on forecasts of tropical cyclone (TC) structure and intensity. VED uncertainties in PBL parameterizations can be partly attributed to the model's inability to represent roll vortices (RV). In this study, RV effects on turbulent fluxes derived from a large eddy simulation (LES) by Li et al. (Geophys. Res. Lett., 2021,48, e2020GL090703) are added to the VED parameterization of the PBL scheme within the operational Hurricane Weather Research and Forecasting (HWRF) model. RV contribution to VED is parameterized through a coefficient and varies with the RV intensity and velocity scale. A modification over land has also been implemented. This modified VED parameterization is compared with the original wind-speed-dependent VED scheme in HWRF. Retrospective HWRF forecasts of Hurricanes Florence (2018) and Laura (2020) are analyzed to evaluate the impacts of the modified VED scheme on landfalling hurricane forecasts. Results show that the modified PBL scheme with the RV effect leads to an improvement in 10-m maximum wind speed forecasts of 14%-31%, with a neutral to positive improvement for track forecasts. Improved wind structure and precipitation forecasts against observations are also noted with the modified PBL scheme. Further diagnoses indicate that the revised PBL scheme enhances moist entropy in the boundary layer over land, leading to improved TC intensity prediction compared to the original scheme. [ABSTRACT FROM AUTHOR]