Deposition and accumulation of aerosol particles on photovoltaics (PV) panels, which is commonly referred to as "soiling of PV panels," impacts the performance of the PV energy system. It is desirable to estimate the soiling effect at different locations and times for modeling the PV system performance and devising cost-effective mitigation. This study presents an approach to estimate the soiling effect by utilizing particulate matter (PM) dry deposition estimates from air quality model simulations. The Community Multiscale Air Quality (CMAQ) modeling system used in this study was developed by the U.S. Environmental Protection Agency (U.S. EPA) for air quality assessments, rule-making, and research. Three deposition estimates based on different surface roughness length parameters assumed in CMAQ were used to illustrate the soling effect in different land-use types. The results were analyzed for three locations in the U.S. for year 2011. One urban and one suburban location in Colorado were selected because there have been field measurements of particle deposition on solar panels and analysis on the consequent soiling effect performed at these locations. The third location is a coastal city in Texas, the City of Brownsville. These three locations have distinct ambient environments. CMAQ underestimates particle deposition by 40% to 80% when compared to the field measurements at the two sites in Colorado due to the underestimations in both the ambient PM₁₀ concentration and deposition velocity. The estimated panel transmittance sensitivity due to the deposited particles is higher than the sensitivity obtained from the measurements in Colorado. The final soiling effect, which is transmittance loss, is estimated as 3.17 ± 4.20% for the Texas site, 0.45 ± 0.33%, and 0.31 ± 0.25% for the Colorado sites. Although the numbers are lower compared to the measurements in Colorado, the results are comparable with the soiling effects observed in U.S.

The Indo-Pacific warm pool (IPWP), which affects the global climate system through supporting tropical convection, has been reported to expand significantly under greenhouse warming. Although early research revealed that the sea surface temperature (SST) threshold for deep convection (σ_conv) increases with global warming, many latest relevant works were still conducted based on the traditional IPWP definition (e.g., static SST = 28 °C threshold, and is referred to as the oceanic warm pool, OWP₂₈). Here, we claim that the OWP₂₈expansion differs from the deep convection favoring pool (DCFP) area change and may not reflect the direct impacts of Indo-Pacific warming on the climate system. Results show that, because of the long-term increase in σ_conv, the DCFP expands at a rate 2.6 times slower than the OWP₂₈from 1979 to 2020. The difference reaches 12–27 times from 2015–2100 under different emission scenarios, based on CMIP6 model simulations. While the OWP₂₈expands to the eastern Pacific, the DCFP will remain within the Indian Ocean and western Pacific Ocean regardless of emission levels. This study emphasizes the necessity of considering the response of the relationship between deep convection and SST to climate change when studying the long-term variability of the IPWP.

This paper describes the vortex initialization (VI) currently used in NCEP operational hurricane models (HWRF and HMON, and possibly HAFS in the future). The VI corrects the background fields for hurricane models: it consists of vortex relocation, and size and intensity corrections. The VI creates an improved background field for the data assimilation and thereby produces an improved analysis for the operational hurricane forecast. The background field after VI can be used as an initial field (as in the HMON model, without data assimilation) or a background field for data assimilation (as in HWRF model).

While there is considerable agreement in the scientific community about the intensification of tropical cyclones (TCs) in a warming world, that consensus does not exist for TC frequency. In order to shed new light on this uncertainty, we classified the global oceans into three pools based on SST percentiles: the (a) warm (≥90th percentiles), (a) moderate (65th–90th percentiles) and (c) cool (<65th percentiles) pools, and found that TC frequency increases significantly over the cool SST pool but decreases in the warm and moderate SST pools. The differences in TC frequency change is large among the three pools, contrasting to the small trend differences of TC intensity.