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Xin Li, Xiaolei Zou, Mingjian Zeng, Ning Wang, and Fei Tang

Abstract

Aimed at improving all-sky Cross-track Infrared Sounder (CrIS) radiance assimilation, this study explores the benefits for CrIS all-sky radiance simulations, focusing on the accuracy of background cloud information, through assimilating cloud liquid water path (LWP), ice water path (IWP), and rain water path (RWP) data retrieved from the Advanced Technology Microwave Sounder (ATMS). The Community Radiative Transfer Model (CRTM), which considers cloud scattering and absorption processes, is applied to simulate CrIS radiances. The Gridpoint Statistical Interpolation ensemble-variational data assimilation (DA) is updated by incorporating ensemble covariances of hydrometeor variables and observation operators of LWP, IWP, and RWP. First, two DA experiments named DActrl and DAcwp are conducted with (DAcwp) and without (DActrl) assimilating ATMS LWP, IWP, and RWP data. Assimilating ATMS cloud retrieval data results in better spatial distributions of hydrometers for both a Meiyu rainfall case and a typhoon case. Analyses of DActrl and DAcwp are then used as input to the CRTM to generate CrIS all-sky radiance simulations SMallsky_DActrl and SMallsky_DAcwp, respectively. Improvements in the DAcwp analyses of hydrometeor variables are found to benefit CrIS radiance simulations, especially in cloudy regions. A long period of statistics reveals that the biases and standard deviations of all-sky observations minus simulations from SMallsky_DAcwp are notably smaller than those from SMallsky_DActrl. This pilot study suggests the potential benefit of combining the use of microwave cloud retrieval products for all-sky infrared DA.

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Thomas W. N. Haine, Renske Gelderloos, Miguel A. Jimenez-Urias, Ali H. Siddiqui, Gerard Lemson, Dimitri Medvedev, Alex Szalay, Ryan P. Abernathey, Mattia Almansi, and Christopher N. Hill

Abstract

Computational Oceanography is the study of ocean phenomena by numerical simulation, especially dynamical and physical phenomena. Progress in information technology has driven exponential growth in the number of global ocean observations and the fidelity of numerical simulations of the ocean in the past few decades. The growth has been exponentially faster for ocean simulations, however. We argue that this faster growth is shifting the importance of field measurements and numerical simulations for oceanographic research. It is leading to the maturation of Computational Oceanography as a branch of marine science on par with observational oceanography. One implication is that ultra-resolved ocean simulations are only loosely constrained by observations. Another implication is that barriers to analyzing the output of such simulations should be removed. Although some specific limits and challenges exist, many opportunities are identified for the future of Computational Oceanography. Most important is the prospect of hybrid computational and observational approaches to advance understanding of the ocean.

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Tristan S. L’Ecuyer, Brian J. Drouin, James Anheuser, Meredith Grames, David Henderson, Xianglei Huang, Brian H. Kahn, Jennifer E. Kay, Boon H. Lim, Marian Mateling, Aronne Merrelli, Nathaniel B. Miller, Sharmila Padmanabhan, Colten Peterson, Nicole-Jeanne Schlegel, Mary L. White, and Yan Xie

Abstract

The 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 light weight, low-power, ambient temperature detectors capable of measuring at wavelengths up to 50 micrometers 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.

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Radan Huth and Martin Dubrovský

Abstract

Studies detecting trends in climate elements typically concentrate on their local significance, ignoring the question on whether the significant local trends may or may not have occurred due to chance. The present paper fills this gap by examining several approaches to detecting statistical significance of trends defined on a grid, that is on a regional scale. To this end, we introduce a novel simple procedure of significance testing, which is based on counting signs of local trends (sign test), and compare it with five other approaches to testing collective significance of trends (counting, extended Mann-Kendall, Walker, fdr, and regression tests). Synthetic data are used to construct null distributions of trend statistics, to determine critical values of the tests, and to assess the performance of tests in terms of type II error. For lower values of spatial and temporal autocorrelations, the sign test and extended Mann-Kendall test perform slightly better than the counting test; these three tests outperform Walker, fdr, and regression tests by quite a wide margin. For high autocorrelations, which is a more realistic case, all tests become similar in their performance, with the exception of the regression test, which performs somewhat worse. Some tests cannot be used under specific conditions because of their construction: Walker and fdr tests for high temporal autocorrelations; sign test under high spatial autocorrelations.

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Momme C. Hell, Bruce D. Cornuelle, Sarah T. Gille, and Nicholas J. Lutsko

Abstract

Southern Ocean (SO) surface winds are essential for ventilating the upper ocean by bringing heat and CO2 to the ocean interior. The relationships between mixed-layer ventilation, the Southern Annular Mode (SAM), and the storm tracks remain unclear because processes can be governed by short-term wind events as well as long-term means.

In this study, observed time-varying 5-day probability density functions (PDFs) of ERA5 surface winds and stresses over the SO are used in a singular value decomposition to derive a linearly independent set of empirical basis functions. The first modes of wind (72% of the total wind variance) and stress (74% of the total stress variance) are highly correlated with a standard SAM index (r = 0.82) and reflect SAM’s role in driving cyclone intensity and, in turn, extreme westerly winds. This Joint PDFs of zonal and meridional wind show that southerly and less westerly winds associated with strong mixed-layer ventilation are more frequent during short and distinct negative SAM phases. The probability of these short-term events might be related to mid-latitude atmospheric circulation. The second mode describes seasonal changes in the wind variance (16% of the total variance) that are uncorrelated with the first mode.

The analysis produces similar results when repeated using 5-day PDFs from a suite of scatterometer products. Differences between wind product PDFs resemble the first mode of the PDFs. Together, these results show a strong correlation between surface stress PDFs and the leading modes of atmospheric variability, suggesting that empirical modes can serve as a novel pathway for understanding differences and variability of surface stress PDFs.

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Jason Naylor and Aaron D. Kennedy

Abstract

This study analyzes the frequency of strong, isolated convective cells in the vicinity of Louisville, Kentucky. Data from the Severe Weather Data Inventory (SWDI) are used to compare the frequency of convective activity over Louisville to the observed frequency at nearby rural locations from 2003–2019. The results show that Louisville experiences significantly more isolated convective activity compared to the rural locations. The difference in convective activity between Louisville and the rural locations is strongest during summer, with peak differences occurring between May and August. Compared to the rural locations, Louisville experiences more isolated convective activity in the afternoon and early evening, but less activity after midnight and into the early morning. Isolated convective events over Louisville are most likely during quiescent synoptic conditions, while rural events are more likely during active synoptic patterns.

To determine if these differences can be attributed primarily to urban effects, two additional cities are shown for comparison—Nashville, Tennessee and Cincinnati, Ohio. Both Nashville and Cincinnati experience more isolated convective activity than all five of their nearby rural comparison areas, but the results for both are statistically significant at four of the five rural locations. In addition, the analysis of Cincinnati includes a sixth comparison site that overlaps the urbanized area of Columbus, Ohio. For that location, differences in convective activity are not statistically significant.

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Xiaohe An, Bo Wu, Tianjun Zhou, and Bo Liu

Abstract

Interdecadal Pacific Oscillation (IPO) and Atlantic Multidecadal Oscillation (AMO), two leading modes of decadal climate variability, are not independent. It was proposed that ENSO-like sea surface temperature (SST) variations play a central role in the Pacific responses to the AMO forcing. However, observational analyses indicate that the AMO-related SST anomalies in the tropical Pacific are far weaker than those in the extratropical North Pacific. Here, we show that SST in the North Pacific is tied to the AMO forcing by convective heating associated with precipitation over the tropical Pacific, instead of by SST there, based on an ensemble of pacemaker experiments with North Atlantic SST restored to the observation in a coupled general circulation model. The AMO modulates precipitation over the equatorial and tropical southwestern Pacific through exciting an anomalous zonal circulation and an interhemispheric asymmetry of net moist static energy input into the atmosphere. The convective heating associated with the precipitation anomalies drive SST variations in the North Pacific through a teleconnection, but remarkably weaken the ENSO-like SST anomalies through a thermocline damping effect. This study has implications that the IPO is a combined mode generated by both AMO forcing and local air-sea interactions, but the IPO-related global-warming acceleration/slowdown is independent of the AMO.

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Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

Abstract

We present a novel approach for the automated quality control (QC) of precipitation for a sparse station observation network within the complex terrain of British Columbia, Canada. Our QC approach uses Convolutional Neural Networks (CNNs) to classify bad observation values, incorporating a multi-classifier ensemble to achieve better QC performance. We train CNNs using human QC’d labels from 2016 to 2017 with gridded precipitation and elevation analyses as inputs. Based on the classification evaluation metrics, our QC approach shows reliable and robust performance across different geographical environments (e.g., coastal and inland mountains), with 0.927 Area Under Curve (AUC) and type I/type II error lower than 15%. Based on the saliency-map-based interpretation studies, we explain the success of CNN-based QC by showing that it can capture the precipitation patterns around, and upstream of the station locations. This automated QC approach is an option for eliminating bad observations for various applications, including the pre-processing of training datasets for machine learning. It can be used in conjunction with human QC to improve upon what could be accomplished with either method alone.

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Kieran M. R. Hunt, Andrew G. Turner, and Reinhard K. H. Schiemann

Abstract

Interactions over South Asia between tropical depressions (TDs) and extratropical storms known as western disturbances (WDs) are known to cause extreme precipitation events, including those responsible for the 2013 floods over northern India. In this study, existing databases of WD and TD tracks are used to identify potential WD–TD interactions from 1979–2015; these are filtered according to proximity and intensity, leaving 59 cases which form the basis of this paper. Synoptic charts, vorticity budgets, and moisture trajectory analyses are employed to identify and elucidate common interaction types among these cases. Two broad families of interaction emerge. Firstly, a dynamical coupling of the WD and TD, whereby either the upper- and lower-level vortices superpose (a vortex merger), or the TD is intensified as it passes into the entrance region of a jet streak associated with the WD (a jet-streak excitation). Secondly, a moisture exchange between the WD and TD, whereby either anomalous moisture is advected from the TD to the WD, resulting in anomalous precipitation near the WD (a TD-to-WD moisture exchange), or anomalous moisture is advected from the WD to the TD (a WD-to-TD moisture exchange). Interactions are most common in the post-monsoon period as the subtropical jet, which brings WDs to the subcontinent, returns south; there is a smaller peak in May and June, driven by monsoon onset vortices. Precipitation is heaviest in dynamically-coupled interactions, particularly jet-streak excitations. Criteria for automated identification of interaction types are proposed, and schematics for each type are presented to highlight key mechanisms.

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Cara L. Cuite, Rebecca E. Morss, Julie L. Demuth, and William K. Hallman

Abstract

Both hurricanes and nor’easters can be destructive and deadly. The current study investigates whether, when all other features of a storm warning message are held constant, people perceive the risks posed by nor’easters and hurricanes differently and whether these differences affect their attitudes and decisions about taking protective action. We conducted an online experiment involving 1,700 Americans residing in Northeastern coastal ZIP codes to test the effects of storm type (hurricane vs. nor’easter). Participants were told that their area was under an evacuation order due to either a predicted hurricane or nor’easter. Reported message comprehension and perceived relevance were similar across storm type; however, storm type had small but significant effects on other dependent measures. Those in the hurricane condition were more likely to believe the storm would be severe (p =. 007). They were also more likely to say that it is important to evacuate, that they would evacuate their homes, and that they would recommend to their neighbors that they evacuate (ps <.001). Additional analysis demonstrated that the effect of storm type on evacuation likelihood is mediated, at least in part, by perceived severity. These findings provide evidence that people perceive hurricanes as more severe and more likely to require taking protective action than nor’easters, even when other attributes of the storms remain the same. Forecasters, broadcast meteorologists, and emergency management professionals should consider these small but important differences in perceptions when communicating about these types of storms.

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