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Gijs de Boer
,
Brian J. Butterworth
,
Jack S. Elston
,
Adam Houston
,
Elizabeth Pillar-Little
,
Brian Argrow
,
Tyler M. Bell
,
Phillip Chilson
,
Christopher Choate
,
Brian R. Greene
,
Ashraful Islam
,
Ryan Martz
,
Michael Rhodes
,
Daniel Rico
,
Maciej Stachura
,
Francesca M. Lappin
,
Antonio R. Segales
,
Seabrooke Whyte
, and
Matthew Wilson

Abstract

Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

Open access
Marybeth C. Arcodia
,
Emily Becker
, and
Ben P. Kirtman

Abstract

Climate variability affects sea levels as certain climate modes can accelerate or decelerate the rising sea level trend, but subseasonal variability of coastal sea levels is underexplored. This study is the first to investigate how remote tropical forcing from the MJO and ENSO impact subseasonal U.S. coastal sea level variability. Here, composite analyses using tide gauge data from six coastal regions along the U.S. East and West Coasts reveal influences on sea level anomalies from both the MJO and ENSO. Tropical MJO deep convection forces a signal that results in U.S. coastal sea level anomalies that vary based on MJO phase. Further, ENSO is shown to modulate both the MJO sea level response and background state of the teleconnections. The sea level anomalies can be significantly enhanced or weakened by the MJO-associated anomaly along the East Coast due to constructive or destructive interference with the ENSO-associated anomaly, respectively. The West Coast anomaly is found to be dominated by ENSO. We examine physical mechanisms by which MJO and ENSO teleconnections impact coastal sea levels and find consistent relationships between low-level winds and sea level pressure that are spatially varying drivers of the variability. Two case studies reveal how MJO and ENSO teleconnection interference played a role in notable coastal flooding events. Much of the focus on sea level rise concerns the long-term trend associated with anthropogenic warming, but on shorter time scales, we find subseasonal climate variability has the potential to exacerbate the regional coastal flooding impacts.

Significance Statement

Coastal flooding due to sea level rise is increasingly threatening communities, but natural fluctuations of coastal sea levels can exacerbate the human-caused sea level rise trend. This study assesses the role of tropical influences on coastal subseasonal (2 weeks–3 months) sea level heights. Further, we explore the mechanisms responsible, particularly for constructive interference of signals contributing to coastal flooding events. Subseasonal signals amplify or suppress the lower-frequency signals, resulting in higher or lower sea level heights than those expected from known climate modes (e.g., ENSO). Low-level onshore winds and reduced sea level pressure connected to the tropical phenomena are shown to be indicators of increased U.S. coastal sea levels, and vice versa. Two case studies reveal how MJO and ENSO teleconnection interference played a role in notable coastal flooding events. Much of the focus on sea level rise concerns the long-term trend associated with anthropogenic warming, but on shorter time scales, we find subseasonal climate variability has the potential to exacerbate the regional coastal flooding impacts.

Restricted access
Nina Horat
and
Sebastian Lerch

Abstract

Subseasonal weather forecasts are becoming increasingly important for a range of socioeconomic activities. However, the predictive ability of physical weather models is very limited on these time scales. We propose four postprocessing methods based on convolutional neural networks to improve subseasonal forecasts by correcting systematic errors of numerical weather prediction models. Our postprocessing models operate directly on spatial input fields and are therefore able to retain spatial relationships and to generate spatially homogeneous predictions. They produce global probabilistic tercile forecasts for biweekly aggregates of temperature and precipitation for weeks 3–4 and 5–6. In a case study based on a public forecasting challenge organized by the World Meteorological Organization, our postprocessing models outperform the bias-corrected forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), and achieve improvements over climatological forecasts for all considered variables and lead times. We compare several model architectures and training modes and demonstrate that all approaches lead to skillful and well-calibrated probabilistic forecasts. The good calibration of the postprocessed forecasts emphasizes that our postprocessing models reliably quantify the forecast uncertainty based on deterministic input information in the form of ECMWF ensemble mean forecast fields only.

Open access
Xiuzhen Li
,
Donghai Wang
, and
Wen Zhou

Abstract

South China encountered an exceptionally heavy pre-summer rainy season in 2022 with the regional precipitation ranking first in the past 44 years. This study aims to analyze the multiple-time-scale variations of precipitation in this pre-summer rainy season to shed light on the complex dynamics influencing pre-summer precipitation over South China. The findings reveal that the variation of precipitation was dominated by the 10–20-day oscillation during April–May, while interannual variation and trend during May–June. The 10–20-day oscillation of precipitation in pre-summer rainy season in South China demonstrates a strong association with cold-air activity, which can be traced back to the propagation of disturbances along a teleconnection, which represents the dominant mode of intraseasonal atmospheric circulation over Eurasia in high latitudes during April–May. This teleconnection plays a crucial role in facilitating cold-air invasion and triggering precipitation over East China and South China. The interannual component of abnormal precipitation is strong during May–June of 2022. It is primarily attributed to the abnormal highs in the lower troposphere over the subtropical western North Pacific and Japan. These abnormal highs are likely stimulated by the combined influences of Eurasian teleconnection propagation and cooling sea surface temperature anomalies (SSTAs) over the tropical central and eastern Pacific in the third year of a consecutive La Niña event. However, the universality of the impact of Eurasian teleconnection propagation on the abnormal high over Japan on interannual scale necessitates further investigation. Furthermore, there is a significant upward trend in pre-summer rainfall over South China, accounting for 38% of the total anomaly observed in 2022.

Restricted access
Ryosuke Okugawa
,
Kazuaki Yasunaga
,
Atsushi Hamada
, and
Satoru Yokoi

Abstract

Large amounts of tropical precipitation have been observed as significantly concentrated over the western coast of Sumatra Island. In the present study, we used a cloud-resolving model to perform 14-day numerical simulations and reproduce the distinctive precipitation distributions over western Sumatra Island and adjacent areas. The control experiment, in which the warmer sea surface temperature (SST) near the coast was incorporated and the terminal velocity and effective radius of ice clouds were parameterized to be temperature dependent, adequately reproduced the precipitation concentration as well as the diurnal cycles of precipitation. We then used the column-integrated frozen moist static energy budget equation, which is virtually equivalent to the column-integrated moisture budget equation under the weak temperature gradient assumption, to formulate sensitivity experiments focusing on the effects of coastal SST and upper-level ice clouds. Analysis of the time-averaged fields revealed that the column-integrated moisture and precipitation in the coast were significantly reduced when a cooler coastal SST or larger ice cloud particle size was assumed. Based on the comparison of the sensitivity experiments and in situ observations, we speculate that ice clouds, which are exported from inland convection that is strictly regulated by solar radiation, promote the accumulation of moisture in the coastal region by mitigating radiative cooling. Together with the moisture and heat supplied by the warm ocean surface, they contribute to the large amounts of precipitation here.

Open access
Azusa Takeishi
and
Chien Wang

Abstract

Raindrop formation processes in warm clouds mainly consist of condensation and collision–coalescence of small cloud droplets. Once raindrops form, they can continue growing through collection of cloud droplets and self-collection. In this study, we develop novel emulators to represent raindrop formation as a function of various physical or background environmental conditions by using a sophisticated aerosol–cloud model containing 300 droplet size bins and machine learning methods. The emulators are then implemented in two microphysics schemes in the Weather Research and Forecasting Model and tested in two idealized cases. The simulations of shallow convection with the emulators show a clear enhancement of raindrop formation compared to the original simulations, regardless of the scheme in which they were embedded. On the other hand, the simulations of deep convection show a more complex response to the implementation of the emulators, in terms of the changes in the amount of rainfall, due to the larger number of microphysical processes involved in the cloud system (i.e., ice-phase processes). Our results suggest the potential of emulators to replace the conventional parameterizations, which may allow us to improve the representation of physical processes at an affordable computational expense.

Significance Statement

Formation of raindrops marks a critical stage in cloud evolution. Accurate representations of raindrop formation processes require detailed calculations of cloud droplet growth processes. These calculations are often not affordable in weather and climate models as they are computationally expensive due to their complex dependence on cloud droplet size distributions and dynamical conditions. As a result, simplified parameterizations are more frequently used. In our study we trained machine learning models to learn raindrop formation rates from detailed calculations of cloud droplet evolutions in 1000 parcel-model simulations. The implementation of the developed models or the emulators in a weather forecasting model shows a change in the total rainfall and cloud characteristics, indicating the potential improvement of cloud representations in models if these emulators replace the conventional parameterizations.

Open access
Víctor C. Mayta
and
Ángel F. Adames Corraliza

Abstract

Observations of column water vapor in the tropics show significant variations in space and time, indicating that it is strongly influenced by the passage of weather systems. It is hypothesized that many of the influencing systems are moisture modes, systems whose thermodynamics are governed by moisture. On the basis of four objective criteria, results suggest that all oceanic convectively coupled tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These modes occur where the horizontal column moisture gradient is steep and not where the column water vapor content is high. Despite geographical basic-state differences, the moisture modes are driven by the same mechanisms across all basins. The moist static energy (MSE) anomalies propagate westward by horizontal moisture advection by the trade winds. Their growth is determined by the advection of background moisture by the anomalous meridional winds and anomalous radiative heating. Horizontal maps of column moisture and 850-hPa streamfunction show that convection is partially collocated with the low-level circulation in nearly all the waves. Both this structure and the process of growth indicate that the moisture modes grow from moisture–vortex instability. Last, space–time spectral analysis reveals that column moisture and low-level meridional winds are coherent and exhibit a phasing that is consistent with a poleward latent energy transport. Collectively, these results indicate that moisture modes are ubiquitous across the tropics. That they occur in regions of steep horizontal moisture gradients and grow from moisture–vortex instability suggests that these gradients are inherently unstable and are subject to continuous stirring.

Significance Statement

Over the tropics, column water vapor has been found to be highly correlated with precipitation, especially in slowly evolving systems. These observations and theory support the hypothesis that moisture modes exist, a type of precipitating weather system that does not exist in dry theory. In this study, we found that all oceanic tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These systems exist in regions where moisture varies greatly in space, and they grow by transporting air from the humid areas of the tropics toward their low pressure center. These results indicate that the climatological-mean distribution of moisture in the tropics is unstable and is subject to stirring by moisture modes.

Open access
Matthieu Kohl
and
Paul A. O’Gorman

Abstract

The vertical velocity distribution in the atmosphere is asymmetric with stronger upward than downward motion. This asymmetry is important for the distribution of precipitation and its extremes and for an effective static stability that has been used to represent the effects of latent heating on extratropical eddies. Idealized GCM simulations show that the asymmetry increases as the climate warms, but current moist dynamical theories based around small-amplitude modes greatly overestimate the increase in asymmetry with warming found in the simulations. Here, we first analyze the changes in asymmetry with warming using numerical inversions of a moist quasigeostrophic omega equation applied to output from the idealized GCM. The inversions show that increases in the asymmetry with warming in the GCM simulations are primarily related to decreases in moist static stability on the left-hand side of the moist omega equation, whereas the dynamical forcing on the right-hand side of the omega equation is unskewed and contributes little to the asymmetry of the vertical velocity distribution. By contrast, increases in asymmetry with warming for small-amplitude modes are related to changes in both moist static stability and dynamical forcing leading to enhanced asymmetry in warm climates. We distill these insights into a toy model of the moist omega equation that is solved for a given moist static stability and wavenumber of the dynamical forcing. In comparison to modal theory, the toy model better reproduces the slow increase of the asymmetry with climate warming in the idealized GCM simulations and over the seasonal cycle from winter to summer in reanalysis.

Significance Statement

Upward velocities are stronger than downward velocities in the atmosphere, and this asymmetry is important for the distribution of precipitation because precipitation is linked to upward motion. An important and open question is what sets this asymmetry and how much it increases as the climate warms. Past work has shown that current theories greatly overestimate the increase in asymmetry with warming in idealized simulations. In this work, we develop a more complete theory and show that it is able to better reproduce the slow increase of the asymmetry with warming that is found over the seasonal cycle from winter to summer and in idealized simulations of warming climates.

Restricted access
Alan Shapiro
,
Jason Chiappa
, and
David B. Parsons

Abstract

Weak but persistent synoptic-scale ascent may play a role in the initiation or maintenance of nocturnal convection over the central United States. An analytical model is used to explore the nocturnal low-level jets (NLLJ) and ascent that develop in an idealized diurnally varying frictional (Ekman) boundary layer in a neutrally stratified barotropic environment when the flow aloft is a zonally propagating Rossby wave. Steady-periodic solutions are obtained of the linearized Reynolds-averaged Boussinesq-approximated equations of motion on a beta plane with an eddy viscosity that is specified to increase abruptly at sunrise and decrease abruptly at sunset. Rayleigh damping terms are used to parameterize momentum loss due to radiation of inertia–gravity waves. The model-predicted vertical velocity is (approximately) proportional to the wavenumber and wave amplitude. There are two main modes of ascent in midlatitudes, an afternoon mode and a nocturnal mode. The latter arises as a gentle but persistent surge induced by the decrease of turbulence at sunset, the same mechanism that triggers inertial oscillations in the Blackadar theory of NLLJs. If the Rayleigh damping terms are omitted, the boundary layer depth becomes infinite at three critical latitudes, and the vertical velocity becomes infinite far above the ground at two of those latitudes. With the damping terms retained, the solution is well behaved. Peak daytime ascent in the model occurs progressively later in the afternoon at more southern locations (in the Northern Hemisphere) until the first (most northern) critical latitude is reached; south of that latitude the nocturnal mode is dominant.

Restricted access
Feihong Zhou
,
Daniel Fiifi Tawia Hagan
,
Guojie Wang
,
X. San Liang
,
Shijie Li
,
Yuhao Shao
,
Emmanuel Yeboah
, and
Xikun Wei

Abstract

The land surface and atmosphere interaction forms an integral part of the climate system. However, this intricate relationship involves many complicated interactions and feedback effects between multiple variables. As a result, relying solely on traditional linear regression analysis and correlation analysis to distinguish between multivariate complex “driver–response” relations can be challenging, since they do not have the needed asymmetry to establish causality. The Liang–Kleeman (LK) information flow theory provides a strict nonparametric causality measurement for identifying the causality between any given time series, and its recent extension from bivariate to multivariate form provides a powerful tool for causal inference in complex multivariate systems. However, the multivariate LK information flow also assumes stationarity in time and requires a sufficiently long time series to ensure statistical sufficiency. To remedy this challenge, we rely on the square-root Kalman filter to estimate the time-varying form of the multivariate LK information flow causality. The results from theoretical and real-world applications show that the new algorithm provides a valuable tool for characterizing time-varying causal relationships in land–atmosphere interactions, even when the time series are short and highly correlated.

Significance Statement

Causality in land–atmosphere interactions is generally characterized by seasonal and intraseasonal changes that are usually not captured with commonly used approaches, because most approaches assume the time series are stationary. In this study, we extend the recently proposed multivariate Liang–Kleeman information flow causality (MtvLK) to handle nonstationary systems such as those in land–atmosphere interactions. By considering nonstationarity, we aim to unravel time-varying causal structures that are usually masked out in commonly used methods. Validating the MtvLK with synthetic models showed that the MtvLK is able to obtain the expected causal structures. Furthermore, real-world applications reveal novel findings of the time-varying causal structures between soil moisture, vapor pressure deficit, and the gross primary product.

Restricted access