Embedding online, design-focused data visualization instruction in an upper-division undergraduate atmospheric science course

An interdisciplinary undergraduate atmospheric science modeling course was cotaught at a midsize public university by three professors from atmospheric science, statistics, and design using a blended learning approach. The in-class portion of the course taught upper-level students numerical weather prediction modeling and statistical evaluation methods. Online modules were used to teach data visualization techniques and three foundational design principles upon which their efficacy depends : legibility, visual hierarchy, and appropriate use of color. Geoscience students need data visualization skills to prepare for careers in government, industry, and research that increasingly require work with big data and communication with diverse collaborators and audiences. This article focuses on the instructional approach for the visualization component of the course—specifically, the three teaching innovations used: online modules, an interdisciplinary teaching team, and design-focused data visualization instruction. Although course enrollment was low at four students, several valuable lessons were learned that can improve the teaching of visualization in geoscience courses, including the utility of structuring visualization instruction around two separate but complementary visualization skills: visualization for analysis and visualization for sharing knowledge. Evaluation of students’ visualization work at the conclusion of the course demonstrated improvement in the foundational design principles as well as improved ability to select appropriate visualization strategies for different situations. The methods used to assess these improvements are presented alongside illustrative examples of student work. Pre- and postcourse surveys indicate the students felt more confident in creating data visualizations upon completion of the course, and qualitative assessments of student work confirm increased application of foundational design principles in visualizations created by the students. The authors argue that teaching visualization as an online supplement to other geoscience instruction is a potentially replicable model for improving students’ learning about visualization. This is especially true when such instruction relies on open-source programs and materials and leverages interdisciplinary expertise in course design

Evaluation of Surface Fluxes in the WRF Model: Case Study for Farmland in Rolling Terrain

The partitioning of available energy into surface sensible and latent heat fluxes impacts the accuracy of simulated near surface temperature and humidity in numerical weather prediction models. This case study evaluates the performance of the Weather Research and Forecasting (WRF) model on the simulation of surface heat fluxes using field observations collected from a surface flux tower in Oregon, USA. Further, WRF-modeled heat flux sensitivities to North American Mesoscale (NAM) and North American Regional Reanalysis (NARR) large-scale input forcing datasets; U.S. Geological Survey (USGS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) land use datasets; Pleim-Xiu (PX) and Noah land surface models (LSM); Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) schemes using the Noah LSM; and Asymmetric Convective Model version 2 (ACM2) PBL scheme using PX LSM are investigated. The errors for simulating 2-m temperature, 2-m humidity, and 10-m wind speed were reduced on average when using NAM compared with NARR. Simulated friction velocity had a positive bias on average, with the YSU PBL scheme producing the largest overestimation in the innermost domain (0.5 km horizontal grid resolution). The simulated surface sensible heat flux had a similar temporal behavior as the observations but with a larger magnitude. The PX LSM produced lower and more reliable sensible heat fluxes compared with the Noah LSM. However, Noah latent heat fluxes were improved with a lower RMSE compared to PX, when NARR forcing data was used. Overall, these results suggest that there is not one WRF configuration that performs best for all the simulated variables (surface heat fluxes and meteorological variables) and situations (day and night)

Development of Pm2.5 Source Impact Spatial Fields Using a Hybrid Source Apportionment Air Quality Model

An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). There is also a growing desire to directly use source impact estimates in health studies; however, source impacts cannot be directly measured. Several limitations are inherent in most source apportionment methods motivating the development of a novel hybrid approach that is used to estimate source impacts by combining the capabilities of receptor models (RMs) and chemical transport models (CTMs). The hybrid CTM–RM method calculates adjustment factors to refine the CTM-estimated impact of sources at monitoring sites using pollutant species observations and the results of CTM sensitivity analyses, though it does not directly generate spatial source impact fields. The CTM used here is the Community Multiscale Air Quality (CMAQ) model, and the RM approach is based on the chemical mass balance (CMB) model. This work presents a method that utilizes kriging to spatially interpolate source-specific impact adjustment factors to generate revised CTM source impact fields from the CTM–RM method results, and is applied for January 2004 over the continental United States. The kriging step is evaluated using data withholding and by comparing results to data from alternative networks. Data withholding also provides an estimate of method uncertainty. Directly applied (hybrid, HYB) and spatially interpolated (spatial hybrid, SH) hybrid adjustment factors at withheld observation sites had a correlation coefficient of 0.89, a linear regression slope of 0.83 ± 0.02, and an intercept of 0.14 ± 0.02. Refined source contributions reflect current knowledge of PM emissions (e.g., significant differences in biomass burning impact fields). Concentrations of 19 species and total PM2.5 mass were reconstructed for withheld observation sites using HYB and SH adjustment factors. The mean concentrations of total PM2.5 at withheld observation sites were 11.7 (± 8.3), 16.3 (± 11), 8.59 (± 4.7), and 9.2 (± 5.7) ?g m?3 for the observations, CTM, HYB, and SH predictions, respectively. Correlations improved for concentrations of major ions, including nitrate (CMAQ–DDM (decoupled direct method): 0.404, SH: 0.449), ammonium (CMAQ–DDM: 0.454, SH: 0.492), and sulfate (CMAQ–DDM: 0.706, SH: 0.730). Errors in simulated concentrations of metals were reduced considerably: 295 % (CMAQ–DDM) to 139 % (SH) for vanadium; and 1340 % (CMAQ–DDM) to 326 % (SH) for manganese. Errors in simulated concentrations of some metals are expected to remain given the uncertainties in source profiles. Species concentrations were reconstructed using SH results, and the error relative to observed concentrations was greatly reduced as compared to CTM-simulated concentrations. Results demonstrate that the hybrid method along with a spatial extension can be used for large-scale, spatially resolved source apportionment studies where observational data are spatially and temporally limited