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Benjamin Pohl, Thomas Saucède, Vincent Favier, Julien Pergaud, Deborah Verfaillie, Jean-Pierre Féral, Ylber Krasniqi, and Yves Richard

Abstract

Daily weather regimes are defined around the Kerguelen Islands (Southern Ocean) based on daily 500 hPa geopotential height anomalies derived from the ERA5 ensemble reanalysis over the period 1979-2018. Ten regimes are retained as significant. Their occurrences are highly consistent across reanalysis ensemble members. Regimes show weak seasonality and non-significant long-term trends in their occurrences. Their sequences are usually short (1-3 days), with extreme persistence values above 10 days. Seasonal regime frequency is mostly driven by the phase of the Southern Annular Mode over Antarctica, mid-latitude dynamics over the Southern Ocean like the Pacific South American mode, and to a lesser extent, tropical variability, with significant but weaker relationships with El Niño Southern Oscillation. At the local scale over the Kerguelen Islands, regimes have a strong influence on measured atmospheric and oceanic variables, including minimum and maximum air temperature, mostly driven by horizontal advections, sea water temperature recorded 5 m below the surface, wind speed and sea level pressure. Relationships are weaker for precipitation amounts. Regimes also modify regional contrasts between observational sites in Kerguelen, highlighting strong exposure contrasts. The regimes allow improving our understanding of weather and climate variability and interactions in this region; they will be used in future work to assess past and projected long-term circulation changes in the southern mid-latitudes.

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Daeho Jin, Lazaros Oreopoulos, Dongmin Lee, Jackson Tan, and Nayeong Cho

Abstract

In order to better understand cloud-precipitation relationships, we extend the concept of cloud regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), to include precipitation information. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, we derive cloud-precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1-degree grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics.

In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud-precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies where characterization of the diurnal cycle is essential.

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Bhupendra A. Raut, Robert Jackson, Mark Picel, Scott M. Collis, Martin Bergemann, and Christian Jakob

Abstract

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.

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Liang Chang, Shiqiang Wen, Guoping Gao, Zhen Han, Guiping Feng, and Yang Zhang

Abstract

Characteristics of temperature inversions (TIs) and specific humidity inversions (SHIs) and their relationships in three of the latest global reanalyses—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55), and the ERA5—are assessed against in situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect many fewer TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than are seen in the RS data in summer 2008, winter 2015, and spring 2015. The reanalyses generally depict well the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is simulated worst by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. In general, ERA5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.

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Maike F. Holthuijzen, Brian Beckage, Patrick J. Clemins, Dave Higdon, and Jonathan M. Winter

Abstract

High-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

Open access
Cristian Muñoz and David M. Schultz

Abstract

A study of 500-hPa cutoff lows in central Chile during 1979–2017 was conducted to contrast cutoff lows associated with the lowest quartile of daily precipitation amounts (LOW25) with cutoff lows associated with the highest quartile (HIGH25). To understand the differences between low- and high-precipitation cutoff lows, daily precipitation records, radiosonde observations, and reanalyses were used to analyze the three ingredients necessary for deep moist convection (instability, lift, and moisture) at the eastern and equatorial edge of these lows. Instability was generally small, if any, and showed no major differences between LOW25 and HIGH25 events. Synoptic-scale ascent associated with Q-vector convergence also showed little difference between LOW25 and HIGH25 events. In contrast, the moisture distribution around LOW25 and HIGH25 cutoff lows was different, with a moisture plume that was more defined and more intense equatorward of HIGH25 cutoff lows as compared with LOW25 cutoff lows where the moisture plume occurred poleward of the cutoff low. The presence of the moisture plume equatorward of HIGH25 cutoff lows may have contributed to the shorter persistence of HIGH25 events by providing a source for latent-heat release when the moisture plume reached the windward side of the Andes. Indeed, whereas 48% of LOW25 cutoff lows persisted for longer than 72 h, only 25% of HIGH25 cutoff lows did, despite both systems occurring mostly during the rainy season (May–September). The occurrence of an equatorial moisture plume on the eastern and equatorial edge of cutoff lows is fairly common during high-impact precipitation events, and this mechanism could help to explain high-impact precipitation where the occurrence of cutoff lows and moisture plumes is frequent.

Open access
Juan A. Crespo, Catherine M. Naud, and Derek J. Posselt

Abstract

Latent and sensible heat fluxes over the oceans are believed to play an important role in the genesis and evolution of marine-based extratropical cyclones (ETCs) and affect rapid cyclogenesis. Observations of ocean surface heat fluxes are limited from existing in situ and remote sensing platforms, which may not offer sufficient spatial and temporal resolution. In addition, substantial precipitation frequently veils the ocean surface around ETCs, limiting the capacity of spaceborne instruments to observe the surface processes within maturing ETCs. Although designed as a tropics-focused mission, the Cyclone Global Navigation Satellite System (CYGNSS) can observe ocean surface wind speed and heat fluxes within a notable quantity of low-latitude extratropical fronts and cyclones. These observations can assist in understanding how surface processes may play a role in cyclogenesis and evolution. This paper illustrates CYGNSS’s capability to observe extratropical cyclones manifesting in various ocean basins throughout the globe and shows that the observations provide a robust sample of ETCs winds and surface fluxes, as compared with a reanalysis dataset.

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J. Jared Rennie, Michael A. Palecki, Sean P. Heuser, and Howard J. Diamond

Abstract

Extreme heat is one of the most pressing climate risks in the United States and is exacerbated by a warming climate and aging population. Much work in heat health has focused only on temperature-based metrics, which do not fully measure the physiological impact of heat stress on the human body. The U.S. Climate Reference Network (USCRN) consists of 139 sites across the United States and includes meteorological parameters that fully encompass human tolerance to heat, including relative humidity, wind, and solar radiation. Hourly and 5-min observations from USCRN are used to develop heat exposure products, including heat index (HI), apparent temperature (AT), and wet-bulb globe temperature (WBGT). Validation of this product is conducted with nearby airport and mesonet stations, with reanalysis data used to fill in data gaps. Using these derived heat products, two separate analyses are conducted. The first is based on standardized anomalies, which place current heat state in the context of a long-term climate record. In the second study, heat events are classified by time spent at various levels of severity of conditions. There is no consensus as to what defines a heat event, so a comparison of absolute thresholds (i.e., ≥30.0°, 35.0°, and 40.0°C) and relative thresholds (≥90th, 95th, and 98th percentile) will be examined. The efficacy of the product set will be studied using an extreme heat case study in the southeastern United States. While no heat exposure metric is deemed superior, each has their own advantages and caveats, especially in the context of public communication.

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Joseph Sedlar, Laura D. Riihimaki, Kathleen Lantz, and David D. Turner

Abstract

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

Open access
Jennifer Nakamura, Upmanu Lall, Yochanan Kushnir, Patrick A. Harr, and Kyra McCreery

Abstract

We present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, Cluster-Based Climate-Conditioned Hurricane Intensity and Track Simulator (C3-HITS), extends a previous version of HITS. HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA’s Best Track Data, version 2, dataset. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the Niño-3.4 index, representing the eastern equatorial Pacific Ocean sea surface temperature (SST) departure from climatology, and main development region, representing tropical North Atlantic SST departure from climatology in the North Atlantic TC main development region. A track clustering procedure is used to identify track families, and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate-conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season.

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