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Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI 80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.

Precipitation microphysics in tropical cyclones (TCs) are often poorly represented in numerical simulations, which ultimately affects TC structure, evolution, and prediction. This provides a large incentive to better observe and understand the underlying microphysical processes in TCs in order to improve precipitation forecasts and warning operations. 112 TCs from 2014 to 2020 were matched up with overpasses from the NASA Global Precipitation Measurement (GPM) mission Dual‐Frequency Precipitation Radar (DPR) on a global scale to identify cloud properties and associated precipitation processes by quantifying vertical slopes of reflectivity in the liquid and ice phase. Further, vertical profiles of reflectivity were partitioned into different 850–200 hPa shear‐relative quadrants of each storm, different annuli from the storm center, and 5 TC ocean basins globally. Preliminary results showed the highest echo top heights occurred in the Northwest Pacific and Indian Ocean basins, with all basins and quadrants exhibiting median negative slopes of KuPR in the liquid phase. Additionally, all shear‐relative quadrants revealed negative slopes of reflectivity in the ice phase implying ice hydrometeor growth. These findings can potentially be used to improve the representation of cloud properties and the accuracy of the DPR particle size distribution algorithm in TCs. Plain Language Summary: Tropical cyclones play a key role in the Earth's water cycle by transferring heat and moisture from the tropics to the mid‐latitudes, which makes it important to better understand precipitation processes in these systems. As tropical cyclones spend a large portion of their life over the open ocean, the use of ground radar to monitor precipitation is rarely an option, which makes space‐borne radar a useful tool for quantifying precipitation. This study investigates precipitation processes in tropical cyclones on a global scale using space‐borne radar. The results suggest that precipitation mechanisms acting to increase or reduce the size of precipitation particles varied by ocean basin and in different areas relative to the storm center. These differences were determined based off the vertical profiles of radar reflectivity indicating a change in precipitation size and concentration with height. Key Points: A 7‐year global analysis of precipitation microphysics in tropical cyclones using space‐borne radarThere exists differences in microphysical processes in tropical cyclones by distance from the storm center, ocean basin, and shear‐relative quadrantAll ocean basins exhibit negative median slopes of reflectivity in both the liquid and ice phases, suggesting primarily hydrometeor growth [ABSTRACT FROM AUTHOR]

In this study we estimate a time series of regional groundwater anomalies by combining terrestrial water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) satellite mission with in situ soil moisture observations from the Oklahoma Mesonet. Using supplementary data from the Department of Energy's Atmospheric Radiation Measurement (DOE ARM) network, we develop an empirical scaling factor with which to relate the soil moisture variability in the top 75 cm sampled by the Mesonet sites to the total variability in the upper 4 m of the unsaturated zone. By subtracting this estimate of the full unsaturated zone soil moisture anomalies, we arrive at a time series of groundwater anomalies, spatially averaged over a region approximately 280,000 km2 in area. Results are compared to observed well level data from a larger surrounding region, and show consistent phase and relative inter-annual variability.