One way to adapt to and mitigate current and future water scarcity is to manage and store water more efficiently. Reservoirs act as critical buffers to ensure agricultural and municipal water deliveries, mitigate flooding, and generate hydroelectric power, yet they often lose significant amounts of water through evaporation, especially in arid and semiarid regions. Despite this fact, reservoir evaporation has been an inconsistently and inaccurately estimated component of the water cycle within the water resource infrastructure of the arid and semiarid western United States. This paper highlights the increasing importance and challenges of correctly estimating and forecasting reservoir evaporation in the current and future climate, as well as the need to bring new ideas and state-of-the-art practices for the estimation of reservoir evaporation into operational use for modern water resource managers. New ideas and practices include i) improving the estimation of reservoir evaporation using up-to-date knowledge, state-of-the-art instrumentation and numerical models, and innovative experimental designs to diagnose processes and accurately forecast evaporation; ii) improving our understanding of spatial and temporal variations in evaporative water loss from existing reservoirs and transferring this knowledge when expanding reservoirs or siting new ones; and iii) implementing an adaptive management plan that incorporates new knowledge, observations, and forecasts of reservoir evaporation to improve water resource management.

The influence of strong winds on the quality of optical Particle Size Velocity (PARSIVEL) disdrometer measurements is examined with data from Hurricane Ike in 2008 and from convective thunderstorms observed during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) in 2010. This study investigates an artifact in particle size distribution (PSD) measurements that has been observed independently by six stationary PARSIVEL disdrometers. The artifact is characterized by a large number concentration of raindrops with large diameters (>5 mm) and unrealistic fall velocities (<1 m s−1). It is correlated with high wind speeds and is consistently observed by stationary disdrometers but is not observed by articulating disdrometers (instruments whose sampling area is rotated into the wind). The effects of strong winds are further examined with a tilting experiment, in which drops are dripped through the PARSIVEL sampling area while the instrument is tilted at various angles, suggesting that the artifact is caused by particles moving at an angle through the sampling area. Most of the time, this effect occurs when wind speed exceeds 20 m s−1, although it was also observed when wind speed was as low as 10 m s−1. An alternative quality control is tested in which raindrops are removed when their diameters exceed 8 mm and they divert from the fall velocity–diameter relationship. While the quality control does provide more realistic reflectivity values for the stationary disdrometers in strong winds, the number concentration is reduced compared to the observations with an articulating disdrometer.

When studying the influence of microphysics on the near-surface buoyancy tendency in convective thunderstorms, in situ measurements of microphysics near the surface are essential and those are currently not provided by most weather radars. In this study, the deployment of mobile microphysical probes in convective thunderstorms during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) is examined. Microphysical probes consist of an optical Ott Particle Size and Velocity (PARSIVEL) disdrometer that measures particle size and fall velocity distributions and a surface observation station that measures wind, temperature, and humidity. The mobile probe deployment allows for targeted observations within various areas of the storm and coordinated observations with ground-based mobile radars. Quality control schemes necessary for providing reliable observations in severe environments with strong winds and high rainfall rates and particle discrimination schemes for distinguishing between hail, rain, and graupel are discussed. It is demonstrated how raindrop-size distributions for selected cases can be applied to study size-sorting and microphysical processes. The study revealed that the raindrop-size distribution changes rapidly in time and space in convective thunderstorms. Graupel, hailstones, and large raindrops were primarily observed close to the updraft region of thunderstorms in the forward- and rear-flank downdrafts and in the reflectivity hook appendage. Close to the updraft, large raindrops were usually accompanied by an increase in small-sized raindrops, which mainly occurred when the wind speed and standard deviation of the wind speed increased. This increase in small drops could be an indicator of raindrop breakup.

Climate change and population growth have increased demand for water in arid regions. For over half a century, cloud seeding has been evaluated as a technology to increase water supply; statistical approaches have compared seeded to nonseeded events through precipitation gauge analyses. Here, a physically based approach to quantify snowfall from cloud seeding in mountain cloud systems is presented. Areas of precipitation unambiguously attributed to cloud seeding are isolated from natural precipitation (<1 mm h⁻¹). Spatial and temporal evolution of precipitation generated by cloud seeding is then quantified using radar observations and snow gauge measurements. This study uses the approach of combining radar technology and precipitation gauge measurements to quantify the spatial and temporal evolution of snowfall generated from glaciogenic cloud seeding of winter mountain cloud systems and its spatial and temporal evolution. The results represent a critical step toward quantifying cloud seeding impact. For the cases presented, precipitation gauges measured increases between 0.05 and 0.3 mm as precipitation generated by cloud seeding passed over the instruments. The total amount of water generated by cloud seeding ranged from 1.2 × 10⁵ m³ (100 ac ft) for 20 min of cloud seeding, 2.4 × 10⁵ m³ (196 ac ft) for 86 min of seeding to 3.4 x 10⁵ m³ (275 ac ft) for 24 min of cloud seeding. Significance Cloud seeding to increase winter snowpack in mountains has traditionally been evaluated using precipitation gauges and target/control statistics leading mostly to inconclusive results. Here, an approach employing radar and gauges is used to quantify snowfall by first isolating radar returns that are unambiguously the result of cloud seeding in regions with light or no natural precipitation and then quantifying the seeding-induced precipitation at the ground. The spatiotemporal evolution of snowfall from cloud seeding is quantified. Although this study focuses only on three cases, the results are a fundamental step toward understanding cloud seeding efficacy that, for over half a century, has been an unanswered question for water managers wishing to utilize the technology for water resource management.

The influence of ground clutter contamination on the estimation of polarimetric radar parameters, horizontal reflectivity (Z_h), differential reflectivity (Z_dr), correlation coefficient (ρ_hυ), and differential propagation phase (ϕ_dp) was examined. This study aims to derive the critical level of ground clutter contamination for Z_h, Z_dr, ρ_hυ, and ϕ_dp at which ground clutter influence exceeds predefined precision thresholds. Reference data with minimal ground clutter contamination consist of eight precipitation fields measured during three rain events characterized by stratiform and convective precipitation. Data were collected at an elevation angle of 0.8° by the Météo-France operational, polarimetric Doppler C-band weather radar located in Trappes, France, ∼30 km southwest of Paris. Nine different ground clutter signatures, ranging from point targets to more complex signatures typical for mountain ranges or urban obstacles, were added to the precipitation fields. This is done at the level of raw in-phase and quadrature component data in the two polarimetric channels. For each ground clutter signature, 30 simulations were conducted in which the mean reflectivity of ground clutter within the resolution volume varied between being 30 dB higher to 30 dB lower than the mean reflectivity of precipitation. Differences in Z_h, Z_dr, ρ_υ, and ϕ_dp between simulation and reference were shown as a function of ratio between ground clutter and precipitation intensities. As a result of this study, horizontal reflectivity showed the lowest sensitivity to ground clutter contamination. Furthermore, a precision of 1.7 dBZ in Z_h is achieved on average when the precipitation and ground clutter intensities are equal. Requiring a precision of 0.2 dB in Z_dr and 3° in ϕ_dp, the reflectivity of precipitation needs to be on average ∼5.5 and ∼6 dB, respectively, higher compared to the reflectivity of ground clutter. The analysis also indicates that the highest sensitivity to the nine clutter signatures was derived for ρ_hυ. To meet a predefined precision threshold of 0.02, reflectivity of precipitation needs to be ∼13.5 dB higher than the reflectivity of ground clutter.