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A technique is presented that uses attenuated backscatter profiles from the CALIOP satellite lidar to estimate cloud base heights of lower-troposphere liquid clouds (cloud base height below approximately 3 km). Even when clouds are thick enough to attenuate the lidar beam (optical thickness τ≥5), the technique provides cloud base heights by treating the cloud base height of nearby thinner clouds as representative of the surrounding cloud field. Using ground-based ceilometer data, uncertainty estimates for the cloud base height product at retrieval resolution are derived as a function of various properties of the CALIOP lidar profiles. Evaluation of the predicted cloud base heights and their predicted uncertainty using a second statistically independent ceilometer dataset shows that cloud base heights and uncertainties are biased by less than 10 %. Geographic distributions of cloud base height and its uncertainty are presented. In some regions, the uncertainty is found to be substantially smaller than the 480m uncertainty assumed in the A-Train surface downwelling longwave estimate, potentially permitting the most uncertain of the radiative fluxes in the climate system to be better constrained.

We evaluate two high-resolution Lake Michigan air quality simulations during the 2017 Lake Michigan Ozone Study campaign. These air quality simulations employ identical chemical configurations but use different input meteorology. The AP-XM configuration follows the U.S. Environmental Protection Agency (EPA)-recommended modeling practices, whereas the YNT_SSNG employs different parameterization schemes and satellite-based inputs of sea surface temperatures, green vegetative fraction, and soil moisture and temperature. Overall, we find a similar performance in the model simulations of hourly and maximum daily average 8 h (MDA8) ozone, with the AP-XM and YNT_SSNG simulations showing biases of -11.42 and -13.54 ppbv (parts per billion by volume), respectively, during periods when the observed MDA8 was greater than 70 ppbv. However, for the two monitoring sites that observed high-ozone events, the AP-XM simulation better matched observations at Chiwaukee Prairie, and the YNT_SSNG simulation better matched observations at the Sheboygan Kohler-Andrae (KA) State Park. We find that the differences between the two simulations are largest for column amounts of ozone precursors, particularly NO 2. Across three high-ozone events, the YNT_SSNG simulation has a lower NO 2 column bias (0.17×1015 mol cm -2) compared to the AP-XM simulation (0.31×1015 mol cm -2). The YNT_SSNG simulation also has an advantage in that it better captures the structure of the boundary layer and lake breeze during the 2 June high-ozone event, although the timing of the lake breeze is about 3 h too early at Sheboygan. Our results are useful for informing an air quality modeling framework for the Lake Michigan area. [ABSTRACT FROM AUTHOR]

High-resolution simulations were performed to assess the impact of different parameterization schemes, surface initialization datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were run where climatological or coarse-resolution surface initialization datasets were replaced by high-resolution, real-time datasets depicting lake surface temperatures (SST), green vegetation fraction (GVF), and soil moisture and temperature (SOIL). Comparison of a baseline simulation employing a configuration similar to that used at the Environmental Protection Agency ("EPA") to another simulation employing an alternative set of parameterization schemes (referred to as "YNT") showed that the EPA configuration produced more accurate analyses on the outermost 12-km resolution domain, but that the YNT configuration was superior for higher-resolution nests. The diurnal evolution of the surface energy fluxes was similar in both simulations on the 12-km grid but differed greatly on the 1.3-km grid where the EPA simulation had much smaller sensible heat flux during the daytime and physically unrealistic ground heat flux. Switching to the YNT configuration led to substantial decreases in root mean square error for 2-m temperature and 2-m water vapor mixing ratio on the 1.3-km grid. Additional improvements occurred when the high-resolution satellite-derived surface datasets were incorporated into the modeling platform, with the SOIL dataset having the largest positive impact on temperature and water vapor. The GVF and SST datasets also produced more accurate temperature and water vapor analyses, but degradations in wind speed, especially when using the GVF dataset. The most accurate simulations were obtained when using the high-resolution SST and SOIL datasets and analysis nudging above 2 km AGL. [ABSTRACT FROM AUTHOR]

High-resolution simulations were performed to assess the impact of different parameterization schemes, surface initialization datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were run where climatological or coarse-resolution surface initialization datasets were replaced by high-resolution, real-time datasets depicting lake surface temperatures (SST), green vegetation fraction (GVF), and soil moisture and temperature (SOIL). Comparison of a baseline simulation employing a configuration similar to that used at the Environmental Protection Agency ("EPA") to another simulation employing an alternative set of parameterization schemes (referred to as "YNT") showed that the EPA configuration produced more accurate analyses on the outermost 12-km resolution domain, but that the YNT configuration was superior for higher-resolution nests. The diurnal evolution of the surface energy fluxes was similar in both simulations on the 12-km grid but differed greatly on the 1.3-km grid where the EPA simulation had much smaller sensible heat flux during the daytime and physically unrealistic ground heat flux. Switching to the YNT configuration led to substantial decreases in root mean square error for 2-m temperature and 2-m water vapor mixing ratio on the 1.3-km grid. Additional improvements occurred when the high-resolution satellite-derived surface datasets were incorporated into the modeling platform, with the SOIL dataset having the largest positive impact on temperature and water vapor. The GVF and SST datasets also produced more accurate temperature and water vapor analyses, but degradations in wind speed, especially when using the GVF dataset. The most accurate simulations were obtained when using the high-resolution SST and SOIL datasets and analysis nudging above 2 km AGL. [ABSTRACT FROM AUTHOR]

We evaluate two high-resolution Lake Michigan air quality simulations during the 2017 Lake Michigan Ozone Study campaign. These air quality simulations employ identical chemical configurations but use different input meteorology. The "EPA" configuration follows EPA recommended modeling practices, whereas the "YNT SSNG" employs different parameterization schemes and satellite-based inputs of sea surface temperatures, green vegetative fraction, and soil moisture and temperature. Overall, we find similar performance in model simulations of hourly and daily 8-hour maximum (MDA8) ozone, with the EPA and YNT_SSNG simulations showing biases of −13.31 and −13.54 ppbv, respectively during periods when the observed MDA8 was greater than 70 ppbv. However, for the two monitoring sites that observed high ozone events, the EPA simulation better matched observations at Sheboygan KA and the YNT_SSNG simulation better matched observations at Chiwaukee Prairie. We find differences between the two simulations are largest for column amounts of ozone precursors, particularly NO2. Across three high ozone events, the YNT_SSNG simulation has a lower column NO2 bias (0.17 × 1015 molecules/cm2) compared to the EPA simulation (0.35 × 1015 molecules/cm2). The YNT_SSNG simulation also has an advantage in better capturing the structure of the boundary layer and lake breeze during the June 2 high ozone event, although the timing of the lake breeze is about 3 hours too early. Our results are useful in informing an air quality modeling framework for the Lake Michigan area. [ABSTRACT FROM AUTHOR]

Earth's climate is strongly influenced by energy deficits at the poles that emit more thermal energy than they receive from the sun. Energy exchanges between the surface and atmosphere influence the local environment while heat transport from lower latitudes drives midlatitude atmospheric and oceanic circulations. In the Arctic, in particular, local energy imbalances induce strong seasonality in surface–atmosphere heat exchanges and an acute sensitivity to forced climate variations. Despite these important local and global influences, the largest contributions to the polar atmospheric and surface energy budgets have not been fully characterized. The spectral variation of far-infrared radiation that makes up 60% of polar thermal emission has never been systematically measured impeding progress toward consensus in predicted rates of Arctic warming, sea ice decline, and ice sheet melt. Enabled by recent advances in sensor miniaturization and CubeSat technology, the Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will document, for the first time, the spectral, spatial, and temporal variations of polar far-infrared emission. Selected under NASA's Earth Ventures Instrument (EVI) program, PREFIRE will utilize new lightweight, low-power, ambient temperature detectors capable of measuring at wavelengths up to 50 μm to quantify Earth's far-infrared spectrum. Estimates of spectral surface emissivity, water vapor, cloud properties, and the atmospheric greenhouse effect derived from these measurements offer the potential to advance our understanding of the factors that modulate thermal fluxes in the cold, dry conditions characteristic of the polar regions. [ABSTRACT FROM AUTHOR]