Drought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, Soil Moisture is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining vapor pressure deficit (VPD) with soil moisture information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Index (SPI) for six major drought events across the United States including three flash drought events and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPI for flash droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both flash and conventional drought events. The results also show that combining VPD with soil moisture reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and soil moisture information.

Passive Aquatic Listeners (PALs) have been increasingly deployed to collect minute-scale surface oceanic rainfall and wind information, with a sampling area similar to the spaceborne sensor footprints. This provides an unprecedented opportunity to validate satellite precipitation products over oceans. This study evaluates the Global Precipitation Climatology Project (GPCP) daily products, including the widely-used GPCP v1.3 and the newly released GPCP v3.2, over oceans using 58 PALs as references. The study shows that the GPCP performance depends on time scale, region, and rainfall intensity. The two versions of GPCP perform similarly at multi-year and monthly scales, while GPCP v3.2 shows substantial improvements in representing rain occurrence and rain intensity at daily scale. The results also highlight the challenge of precipitation measurement over certain regions such as the tropical Northeastern Pacific and extratropical North Pacific, where both versions of the GPCP products perform similarly but exhibit noticeable differences compared to PAL observations.

Different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non-orographic events. NCEP Stage-IV product used as a reference. All of the evaluated products show more significant errors for the orographic than the non-orographic events. The Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), Microwave Humidity sounder (MHS), and GPM Microwave Imager (GMI) severely underestimate the precipitation rates, especially for heavy precipitation (> 4 mm/day), whereas infrared precipitation of the Integrated Multi-satellite Retrievals for GPM (IMERG-IR) and a reanalysis product (ERA5) show relatively better estimation. Satellite products tend to show a lower fraction of precipitation occurrence and amount for orographic than no-orographic classes. It was found that rate BIAS varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm-season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (< 10 0C) and lower TPW (<15mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non-orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20 0C), where satellite products tend to overestimate precipitation amounts.