Unlike large, expensive, and high-risk operational satellites, small/cube satellites (SmallSats) are a small, inexpensive, and a low-risk type of satellite. As a NOAA Cooperative Institute with specialties in satellite data processing and data assimilation, CIRA is funded by a Technology Maturation Program (TMP) research project to help NOAA exploit upcoming constellation of SmallSats to be considered for use in operations. In this research, a CSU-led technology demonstration mission entitled “the Temporal Experiment for Storms and Tropical System - Demonstration (TEMPTEST-D)” is used as an example to explore quick and agile methodologies to entrain SmallSats into the NOAA processing stream. Specifically, a workflow that enables TEMPEST-D data for assimilation into the NCEP Global Forecast System (GFS) with Finite-Volume Cube-Sphered (FV3) dycore (FV3GFS) under the Gridpoint Statistical Interpolation (GSI) based hybrid 4DEnVar system is established. One objective of this TMP research project is to assess the impact of SmallSat data on NOAA modeling and assimilation systems used in operations. We begin by asking whether the use of TEMPEST-D data is as good as the use of those obtained from well-established operational satellite sensors. Since the radiometric specification of TEMPEST-D is similar to the Microwave Humidity Sounder (MHS), we address the above question by directly comparing the following three cycled FV3GFS data assimilation and forecasting experiments: 1) the control experiment, which includes all routinely assimilated observations, but only assimilates MHS from the NOAA-19 and MetOp-B platforms, 2) the AddMHS experiment, which is the control plus MHS from the MetOp-A platform, and 3) the AddTEMPEST experiment, which is the control plus TEMPEST-D. By differentiating the AddMHS and AddTEMPEST experiments against the control experiment, we will be able to demonstrate that a cost-effective TEMPEST-D is as beneficial as a well-established operational satellite like MHS, in terms of aiding operational global weather forecasting. In addition, results from this research offers implications of the utility of a constellation of SmallSats microwave radiometers for global weather forecasting.

High concentrations of fecal indicator bacteria (FIB) can result in exceedance of surface water quality standards, particularly in urban areas which receive stormwater runoff. Although FIB are considered to be an indicator of mammalian waste, there is increasing evidence of regrowth in sediments of warm streams. However, the role that sediment-water interactions play on the spatiotemporal distribution of FIB is not well understood, particularly on scales relevant to water quality management. We examined E. coli loads in river water and concentrations in sediments in a 60 km reach of the Rio Grande near Albuquerque, USA to determine seasonal and spatial trends of the coupling between FIB in the water column and those in sediments. Our study reach progresses from a narrow, coarse-bottom channel (0-9.4 km) with insignificant anthropogenic FIB sources to a wide, sand-braided channel with prevalent mid-channel bars and islands (25-61.5 km) that flows through a city with 1 million inhabitants. At upstream sites, we observed low E. coli loads during low discharges and low sediment E. coli concentrations. Conversely, downstream sites had high E. coli loads and high sediment concentrations year-round. Using a simple framework to quantify sediment-water interactions and E. coli transferability, we found that the fine riverbed sediments present at downstream sites served as a source of E. coli to the overlying water and that they promoted the survival of E. coli along this subreach, contributing to frequent violations of the stream standard. Therefore, we propose that sediment-water interactions should be explicitly considered in water quality management, protection, and improvement efforts.

We introduce "The Integrator," a novel technique to quantify transport and reaction metrics commonly used to characterize flow systems. This development consists of two products: (1) The Integrator sampling device and (2) its supporting mathematical framework, which is compatible with semi-continuous sensor data. The use of The Integrator device simplifies the logistics of sample collection and greatly reduces the number of samples needed, making it ideal to characterize systems that are: (1) difficult to access, (2) large and thus intractable or highly heterogeneous, and (3) highly instrumented otherwise but where a more holistic, mechanistic understanding may be gained by monitoring one or more currently untracked elements. We tested and validated The Integrator technique using experimental data collected from a heart rate monitor (high-quality, high-frequency data in response to known excitation events) and solute tracer experiments conducted in two contrasting (fourth and seventh order) rivers. In the Supporting Information, we provide details concerning the design of The Integrator device used in our field case studies and provide insight into potential improvements. Despite our case studies focus on the analysis of conservative and reactive transport of solutes in rivers, the principles behind The Integrator technique can be used to monitor water quality in hyporheic zones, aquifers, wetlands, swamps, karsts, oceans, wastewater treatment plants, pipe networks, and air quality. Furthermore, special arrangements of Integrator devices can be used to gather data at spatial and temporal resolutions that are currently unattainable due to high transportation and/or personnel costs.