Established as a multipurpose network, the Oklahoma Mesonet operates more than 110 surface observing stations that send data every 5 min to an operations center for data quality assurance, product generation, and dissemination. Quality-assured data are available within 5 min of the observation time. Since 1994, the Oklahoma Mesonet has collected 3.5 billion weather and soil observations and produced millions of decision-making products for its customers.

Vegetation strongly influences exchanges of energy and moisture between land and atmosphere through (1) the vegetation's response to incoming radiation and its emission of longwave radiation (2) the vegetation's physical presence, and (3) the plant's transpiration. These processes affect the diurnal temperature range, processes in the atmospheric boundary layer, cloud cover, rainfall, differential heating, and atmospheric circulations. This paper overviews how vegetation interacts with surface energy and moisture budgets and reviews both observational and modelling studies that examine how vegetation affects weather and climate on the mesoscale (ie, phenomena 10s to 100s of kilometres in horizontal size).

Mesoscale meteorological data present their own challenges and advantages during the quality assurance (QA) process because of their variability in both space and time. To ensure data quality, it is important to perform quality control at many different stages (e.g., sensor calibrations, automated tests, and manual assessment). As part of an ongoing refinement of quality assurance procedures, meteorologists with the Oklahoma Mesonet continually review advancements and techniques employed by other networks. This article’s aim is to share those reviews and resources with scientists beginning or enhancing their own QA program. General QA considerations, general automated tests, and variable-specific tests and methods are discussed.

A significant challenge with dynamical downscaling of climate simulations is the ability to accurately represent convection and precipitation. The use of convection‐permitting resolutions avoids cumulus parameterization, which is known to be a large source of uncertainty. A regional climate model (RCM) based on the Weather Research and Forecasting model is configured with a 4 km grid spacing and applied to the U.S. Great Plains, a region characterized by many forms of weather and climate extremes. The 4 km RCM is evaluated by running it in a hindcast mode over the central U.S. region for a 10 year period, forced at the boundary by the 32 km North America Regional Reanalysis. The model is also run at a 25 km grid spacing, but with cumulus parameterization turned on for comparison. The 4 km run more successfully reproduces certain observed features of the Great Plains May‐through‐August precipitation. In particular, the magnitude of extreme precipitation and the diurnal cycle of precipitation over the Great Plains are better simulated. The 4 km run more realistically simulates the low‐level jet and related atmospheric circulations that transport and redistribute moisture from Gulf of Mexico. The convection‐permitting RCM may therefore produce better dynamical downscaling of future climate when nested within global model climate projections, especially for extreme precipitation magnitudes. The 4 km and 25 km simulations do share similar precipitation biases, including low biases over the central Great Plains and high biases over the Rockies. These biases appear linked to circulation biases in the simulations, but determining of the exact causes will require extensive, separate studies.

A novel multisite cascading calibration (MSCC) approach using the shuffled complex evolution–University of Arizona (SCE-UA) optimization method, developed at the University of Arizona, was employed to calibrate the variable infiltration capacity (VIC) model in the Red River Basin. Model simulations were conducted at 35 nested gauging stations. Compared with simulated results using a priori parameters, single-site calibration can improve VIC model performance at specific calibration sites; however, improvement is still limited in upstream locations. The newly developed MSCC approach overcomes this limitation. Simulations using MSCC not only utilize all of the available streamflow observations but also better represent spatial heterogeneities in the model parameters. Results indicate that MSCC largely improves model performance by decreasing the number of stations with negative Nash-Sutcliffe coefficient of efficiency (NSCE) values from 69% (66%) for a priori parameters to 37% (34%) for single-site ...