Eric E. Small’s research while affiliated with University of Colorado Boulder and other places

Soil moisture data with both a fine spatial scale and a short global repeat period would benefit many hydrologic and climatic applications. Since the radar transmitter malfunctioned on NASA’s Soil Moisture Active Passive (SMAP) in 2015, SMAP soil moisture has been downscaled using numerous alternative fine-resolution data. In this paper, we describe the creation and validation of a new downscaled 3 km soil moisture dataset, which is the culmination of previous work. We downscaled SMAP enhanced 9 km brightness temperatures by merging them with L-band Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data, using a modified version of the SMAP active–passive brightness temperature algorithm. We then calculated 3 km SMAP/CYGNSS soil moisture using the resulting 3 km SMAP/CYGNSS brightness temperatures and the SMAP single-channel vertically polarized soil moisture algorithm (SCA-V). To remedy the sparse daily coverage of CYGNSS data at a 3 km spatial resolution, we used spatially interpolated CYGNSS data to downscale SMAP soil moisture. 3 km interpolated SMAP/CYGNSS soil moisture matches the SMAP repeat period of ~2–3 days, providing a soil moisture dataset with both a fine spatial scale and a short repeat period. 3 km interpolated SMAP/CYGNSS soil moisture, upscaled to 9 km, has an average correlation of 0.82 and an average unbiased root mean square difference (ubRMSD) of 0.035 cm³/cm³ using all SMAP 9 km core validation sites (CVSs) within ±38° latitude. The observed (not interpolated) SMAP/CYGNSS soil moisture did not perform as well at the SMAP 9 km CVSs, with an average correlation of 0.68 and an average ubRMSD of 0.048 cm³/cm³. A sensitivity analysis shows that CYGNSS reflectivity is likely responsible for most of the uncertainty in downscaled SMAP/CYGNSS soil moisture. The success of 3 km SMAP/CYGNSS soil moisture demonstrates that Global Navigation Satellite System–Reflectometry (GNSS-R) observations are effective for downscaling soil moisture.

Automated snow station networks provide critical hydrologic data. Whether point observations represent snowpack at larger areas is an enduring question. Leveraging the recent proliferation of airborne lidar snow depth data, we revisit the question of snow station representativeness at multiple scales surrounding 111 stations in Colorado and California (USA) from 2021–2023 (n=476 total samples). In about 50 % of cases, station depths were at least 10 cm higher than areal-mean snow depth (from lidar) at 0.5 to 4 km scales. The nearest 50 m lidar pixels had lower bias and were more often representative of the areal-mean snow depth than coincident stations. The closest 3 m lidar pixel often agreed with station snow depth to within 10 cm, suggesting differences between station snow depth and the nearest 50 m lidar pixel result from highly localized conditions and not the measurement method. Representativeness decreased as scale increased up to ∼6 km, mainly explained by the elevation of a site relative to the larger area. Relative values of vegetation and southness did not have significant impacts on site representativeness. The sign of bias at individual snow stations is temporally consistent, suggesting the relationship between station depth and that of the surrounding area may be predictable. Improving understanding of snow station representativeness could allow for more accurate validation of modeled and remotely sensed data.

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance.

Snow is a critical component of global climate and provides water resources to over 1 billion people worldwide. Yet current measurement methods and modeling techniques lack the ability to fully capture snow characteristics such as snow water equivalent (SWE) and density across variable landscapes. In recent years, physics‐informed machine learning (ML) methods have demonstrated promise for combining data‐driven learning and physical information. However, this capability has not been widely explored within snow hydrology. Here, we develop a “hybrid” model that applies ML informed by outputs from a physical model and assess whether it provides more accurate estimations of SWE and snow density. We trained and evaluated models at 49 SNOw TELemetry locations spanning a range of snow climates in the western US using 9 years of daily data. The research addressed two questions. In the first, the performance of the hybrid model was compared against a plain neural network (long short‐term memory, Long‐Short Term Memory), a high‐quality physical model, and a statistical snow density model. The second question focused on how regionally trained hybrid models compared to a westwide model as well as their transferability between multiple snow regions. The results showed that combining physical information and ML reduced SWE Root Mean Square Error by 35% compared to a physical model and 51% compared to a neural network. Additionally, regional training only provided minimal benefits compared with a westwide model. These findings indicate that a hybrid approach can yield more accurate snowpack characterization than either physical snow models or ML alone.

Automated snow station networks provide critical hydrologic data. Whether point observations represent snowpack at larger areas is an enduring question. Leveraging the recent proliferation of airborne Lidar snow depth data, we revisit the question of snow station representativeness at multiple scales surrounding 111 stations in Colorado and California (U.S.A.) from 2021–2023 (n= 476 total samples). In about 50 % of cases, station depths were at least 10 cm higher than areal-mean snow depth (from Lidar) at 0.5 to 4 km scales. The nearest 50 m Lidar pixels had lower bias and were more often representative than coincident stations. The closest 3 m Lidar pixel often agreed (within 10 cm) with station snow depth, suggesting differences between station snow depth and the nearest 50 m Lidar pixel result from highly localized conditions, not the measurement method. Representativeness decreased as scale increased up to 6 km, mainly explained by the elevation of a site relative to the larger area. The bias direction at individual snow stations is temporally consistent, suggesting the relationship between station depth and that of the surrounding area may be predictable. Improving understanding of snow station representativeness could allow for more accurate validation of modelled and remotely sensed data.

The CYGNSS Handbook was compiled in 2015 to serve as a source of information about the mission and its data products for potential data users. The handbook was updated to describe multiple changes to data processing and products in 2022. Chapter 1 describes the motivation for the project and includes a discussion about tropical cyclones and current technologies for observing and forecasting them, as well as the unique impact the CYGNSS mission will have on the advancement of our scientifc understanding in this field. Chapter 2 outlines the science objectives as well as the baseline mission requirements. Additionally, a mission synopsis explains the different phases of the mission and provides an overview of the flow of information, the ground data processing, and the fight segment hardware required to support the mission. Chapter 3 explains the mission design in detail, including the orbital elements, the observatory, and the science payload. Chapter 4 gives the reader an overview of the data products at each level of processing, including descriptions of their temporal and spatial resolutions. Chapters 5 through 10 provide relevant excerpts from the project Algorithm Theoretical Basis Documents (ATBDs). The ATBDs give the reader a more comprehensive explanation of each level of data product, including the physical and mathematical descriptions of the algorithms used in the generation of the Science Data Products, an explanation of uncertainty estimates, and considerations of calibration, validation, exception control, and diagnostics. Chapter 11 presents both a top-down and a bottom-up assessment of Level 1 (basic observable) and Level 2 (wind speed estimate) uncertainties. The handbook concludes with a list of project publications in Chapter 12 and a list of acronyms in Chapter 13. The document concludes with an appendix describing the ocean surface bistatic scattering forward model. The forward model relates the state of the ocean to the measurements made by CYGNSS and is a fundamental mathematical framework for the science behind the CYGNSS data products.

NASA’s Soil Moisture Active Passive (SMAP) mission only retrieved ~2.5 months of 3 km near surface soil moisture (NSSM) before its radar transmitter malfunctioned. NSSM remains an important area of study, and multiple applications would benefit from 3 km NSSM data. With the goal of creating a 3 km NSSM product, we developed an algorithm to downscale SMAP brightness temperatures (TBs) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data. The purpose of downscaling SMAP TB is to represent the spatial heterogeneity of TB at a finer scale than possible via passive microwave data alone. Our SMAP/CYGNSS TB downscaling algorithm uses β as a scaling factor that adjusts TB based on variations in CYGNSS reflectivity. β is the spatially varying slope of the negative linear relationship between SMAP emissivity (TB divided by surface temperature) and CYGNSS reflectivity. In this paper, we describe the SMAP/CYGNSS TB downscaling algorithm and its uncertainties and we analyze the factors that affect the spatial patterns of SMAP/CYGNSS β. 3 km SMAP/CYGNSS TBs are more spatially heterogeneous than 9 km SMAP enhanced TBs. The median root mean square difference (RMSD) between 3 km SMAP/CYGNSS TBs and 9 km SMAP TBs is 3.03 K. Additionally, 3 km SMAP/CYGNSS TBs capture expected NSSM patterns on the landscape. Lower (more negative) β values yield greater spatial heterogeneity in SMAP/CYGNSS TBs and are generally found in areas with low topographic roughness (<350 m), moderate NSSM variance (~0.01–0.0325), low-to-moderate mean annual precipitation (~0.25–1.5 m), and moderate mean Normalized Difference Vegetation Indices (~0.2–0.6). β values are lowest in croplands and grasslands and highest in forested and barren lands.