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The ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) photon‐counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat‐2 measurements. Careful planning and dedicated development were accomplished during the pre‐launch phase of the mission in preparation for the 2018 launch. Once on‐orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat‐2's contributions to Earth observation science initiated by NASA and the ICESat‐2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat‐2's data capabilities and allowed for advanced science applications beyond the original mission objectives. Plain Language Summary: NASA launched its second Earth observing laser altimeter in 2018 with mission objectives of collecting observations in support of Earth science as a window into climate change impacts on our planet. Pre‐launch studies focused on specific instrument settings and on‐board data processing to support the mission objectives without violating data volume constraints. Once the instrument was on‐orbit and operational, evaluation of the algorithms for success in signal detection, signal finding, and signal telemetry was undertaken. In response to the evaluation, updates have been made to optimize the data provided by the mission. Key Points: Since the 2018 ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) launch multiple updates have been made to the onboard flight receiver algorithm parameters to improve access and utility of the data for a multi‐disciplinary science communityThe adjustments have been primarily in the in the vertical telemetry band settings and have facilitated enhanced observations of blowing snow and increased detection opportunities of bathymetry in nearshore environmentsThe parameter changes have been made to mitigate data losses in certain situations and to advance science applications outside of the primary science objectives of the ICESat‐2 mission [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has collected surface elevation measurements for over 5 years. ICESat‐2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat‐2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat‐2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐NG) grain sizes, and a second to estimate ICESat‐2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS‐NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS‐NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat‐2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat‐2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat‐2 mission, but we cannot rule out more significant errors over coarse‐grained snow. Plain Language Summary: The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has been used to measure changes in land ice, vegetation cover, and sea ice, and there is growing interest to use ICESat‐2 for snow science. ICESat‐2 uses a green laser to estimate the elevation of the Earth's surface, and recent work has shown that snow can introduce errors in green laser measurements such as ICESat‐2. In this study, we used ICESat‐2 and airborne data to (a) identify errors in the ICESat‐2 data and (b) link the errors to snow properties over the Greenland Ice Sheet. We found that ICESat‐2 errors are linked to two snow properties: the size of individual snow grains and the density of the snow. Two retrieval methods were used to estimate snow grain size, though the methods show significant differences when large snow grains are observed. Snow grain size and density are insufficient to fully explain ICESat‐2 biases, so we propose that other factors also play a role. The ICESat‐2 errors are within the accuracy requirements of ICESat‐2, but more significant errors may be possible during the Northern Hemisphere melting season. Key Points: Very large snow grains and densities are needed to explain centimeter‐level differences between ICESat‐2 and airborne lidarBiases from snow grain size and other factors may be influenced by different height measurement methods between ICESat‐2 and airborne lidarSnow grain size retrievals strongly differ between airborne lidar and AVIRIS‐NG over regions with coarse‐grained snow [ABSTRACT FROM AUTHOR]

Because wind farms affect local weather and microclimates, parameterizations of their effects have been developed for numerical weather prediction models. While most wind farm parameterizations (WFP) include drag effects of wind farms, models differ on whether or not an additional turbulent kinetic energy (TKE) source should be included in these parameterizations to simulate the impact of wind farms on the boundary layer. Therefore, we use aircraft measurements above large offshore wind farms in stable conditions to evaluate WFP choices. Of the three case studies we examine, we find the simulated ambient background flow to agree with observations of temperature stratification and winds. This agreement allowing us to explore the sensitivity of simulated wind farm effects with respect to modeling choices such as whether or not to include a TKE source, horizontal resolution, vertical resolution, and advection of TKE. For a stably stratified marine atmospheric boundary layer (MABL), a TKE source and a horizontal resolution in the order of 5 km or finer are necessary to represent the impact of offshore wind farms on the MABL. Additionally, TKE advection results in excessively reduced TKE over the wind farms, which in turn causes an underestimation of the wind speed above the wind farm. Furthermore, using fine vertical resolution increases the agreement of the simulated wind speed with satellite observations of surface wind speed. [ABSTRACT FROM AUTHOR]