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R. M. Samelson
,
S. M. Durski
,
D. B. Chelton
,
E. D. Skyllingstad
, and
P. L. Barbour

Abstract

The dependence of surface-current damping on the definition of surface current for the relative wind is examined in coupled ocean-atmosphere numerical simulations of the northern California Current System (nCCS) during March through October 2009. The model response is analyzed for wind stress computed from relative wind for six different choices of effective model surface velocity. Simulations without surface-current coupling are also considered. As a function of the geographically varying uppermost grid-level depth, the model uppermost grid-level velocity is found to have a wind-drift component with a log-layer structure. Mean geostrophic wind work is concentrated in the shelf and slope regions during March through May (MAM) and in the deep offshore region in June through September (JJAS). The surface-current damping effect on ocean kinetic energy depends more strongly on the parameterization of atmospheric planetary boundary layer (PBL) turbulence than on the surface-current coupling formulation: weaker PBL mixing gives stronger surface-current damping. The damping effect is stronger in the less energetic, offshore region than in the more energetic region closer to the coast. During MAM, the changes in kinetic energy and geostrophic wind-work in the shelf and slope regions are spatially correlated, while during JJAS, the changes in geostrophic wind-work are strongly modulated by SST-stress coupling. The wind-drift-corrected surface-current formulations result in large changes in the effective wind-work based on the product of stress and relative-wind surface current but in only small changes in the kinetic energy of the circulation.

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Christopher M. Hartman
,
Falko Judt
, and
Xingchao Chen

Abstract

Tropical cyclone formation is known to require abundant water vapor in the lower to middle troposphere within the incipient disturbance. In this study, we assess the impacts of local water vapor analysis uncertainty on the predictability of the formation of Hurricane Irma (2017). To this end, we reduce the magnitude of the incipient disturbance’s water vapor perturbations obtained from an ensemble-based data assimilation system that constrained moisture by assimilating all-sky infrared and microwave radiances. Five-day ensemble forecasts are initialized two days before genesis using each set of modified analysis perturbations. Growth of convective differences and intensity uncertainty are evaluated for each ensemble forecast. We observe that when initializing an ensemble forecast with only moisture uncertainty within the incipient disturbance, the resulting intensity uncertainty at every lead time exceeds half that of an ensemble containing initial perturbations to all variables throughout the domain. Although ensembles with different initial moisture uncertainty amplitudes reveal a similar pathway to genesis, uncertainty in genesis timing varies substantially across ensembles since moister members exhibit earlier spinup of the low-level vortex. These differences in genesis timing are traced back to the first 6–12 h of integration, when differences in the position and intensity of mesoscale convective systems across ensemble members develop more quickly with greater initial moisture uncertainty. In addition, the rapid growth of intensity uncertainty may be greatly modulated by the diurnal cycle. Ultimately, this study underscores the importance of targeting the incipient disturbance with high spatiotemporal water vapor observations for ingestion into data assimilation systems.

Significance Statement

Hurricanes form from clusters of thunderstorms that organize into a coherent system. One of the key ingredients for the formation process is an abundance of moisture. In this study, we test the sensitivity of hurricane formation to the initial moisture content in the vicinity of the cluster of thunderstorms that would become Hurricane Irma (2017). To do so, we initialize sets of forecasts each having a different variability of initial moisture content within the embryonic disturbance. Our results show that the predictability of hurricane formation is highly dependent on the uncertainty of the moisture content within the initial disturbance. Consequently, more high-quality observations of the moisture within the precursor disturbances to hurricanes are expected to improve forecasts of their formation.

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Carly R. Tozer
,
James S. Risbey
,
Michael J. Pook
,
Didier P. Monselesan
,
Damien Irving
,
Nandini Ramesh
, and
Doug Richardson

Abstract

Despite common background La Niña conditions, Australia was very dry in November 2020 and wet in November 2021. This paper aims to provide an explanation for this difference. Large-scale drivers of Australian rainfall, including the El Niño Southern Oscillation, Indian Ocean Dipole, Southern Annular Mode and Madden Julian Oscillation, were examined but did not provide obvious clues for the differences. We found that the absence (in 2020) or presence (in 2021) of an enhanced thermal wind and subtropical jet over the Australian continent contributed to the rainfall anomalies. In general, La Niña sets up warm sea surface temperatures around northern Australia, which enhances the meridional temperature gradient over the continent, and hence thermal wind and subtropical jet. In November 2021 these warm sea surface temperatures, coupled with a persistent mid-latitude trough, which advected cold air over the Australian continent, led to an enhanced meridional temperature gradient and subtropical jet over Australia. The enhanced jet provided favourable conditions for the development of rain-bearing weather systems across Australia. In 2020 the continent was warm, displacing the latitude of maximum meridional temperature gradient south of the continent, resulting in fewer instances of the subtropical jet over Australia, and little development of weather systems over the continent. We highlight that although La Niña tilts the odds to wetter conditions for Australia, in any given month, variability in temperatures over the continent can contribute to subtropical jet variability and resulting rainfall in ways which confound the normal expectation from La Niña.

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David Landry
,
Anastase Charantonis
, and
Claire Monteleoni

Abstract

We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface temperature outputs from the Global Deterministic Prediction System up to ten-day lead times, targeting METAR observations in Canada and the United States. We show how postprocessing performance is improved by training a single model for multiple lead times. Multiple strategies to condition the network for the lead time are studied, including a supplementary predictor and an embedding. The proposed model is evaluated for accuracy, spread, distribution calibration, and its behavior under extremes. The neural network approach decreases CRPS by 15% and has improved distribution calibration compared to a naive probabilistic model based on past forecast errors. Our approach increases the value of a deterministic forecast by adding information about the uncertainty, without incurring the cost of simulating multiple trajectories. It applies to any gridded forecast including the recent machine learning-based weather prediction models. It requires no information regarding forecast spread and can be trained to generate probabilistic predictions from any deterministic forecast.

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Matthew D. Flournoy
,
Anthony W. Lyza
,
Andrew R. Wade
, and
Jannick Fischer

Abstract

Cell mergers with supercells are relatively common, but much remains unknown about how they may influence subsequent supercell hazards. Furthermore, many outstanding questions regarding mesocyclone evolution exist despite numerous studies linking supercell hazards with the background environments in which they occur. In this study, we analyze the Multi-Year Reanalysis of Remotely Sensed Storms dataset along with hundreds of observed supercell tracks to begin addressing these ideas. In line with recent studies, the outcome of a supercell-cell merger (specifically the final strength of the low-level supercell mesocyclone) is not strongly related to the background environment. Of the parameters that we tested, mixed-layer (ML) LCL exhibited the largest correlation, but the very small coefficient of determination suggests limited operational use. More significantly, the incorporation of Storm Prediction Center objective analyses yields novel quantification of observed mesocyclone strengths in different environments. Of the environmental characteristics tested, kinematic parameters like 0–3-km storm-relative helicity (SRH) and 0–3-km bulk wind difference are more correlated with peak mesocyclone intensity than thermodynamic variables like CAPE and CIN. 0–3-km SRH exhibits the largest correlation, and its variability explains roughly one-third of the variance of peak azimuthal shear. We show trends in peak mesocyclone intensity across notable environmental parameter spaces, as well as how low-level mesocyclone strength fluctuates as background environmental characteristics evolve. Environmental trends during and preceding the times of peak mesocyclone strength are quantified. These analyses may be useful for predicting short-term mesocyclone intensity changes in real time.

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Sen Yang
,
Deqin Li
,
Xiang-yu Huang
,
Zhiquan Liu
,
Xiao Pan
, and
Yunxia Duan

Abstract

The microphysical parameterization scheme employed in four-dimensional variational data assimilation (4D-Var) plays an important role in the assimilation of humidity and cloud-sensitive observations. In this study, a newly developed full-hydrometeor assimilation scheme, integrating warm-rain and cold-cloud processes, has been implemented in the Weather Research and Forecasting (WRF) 4D-Var system. This scheme is based on the WRF single-moment 6-class microphysics scheme (WSM6). Its primary objective is to directly assimilate radar reflectivity observations, with the goal of evaluating its effects on model initialization and subsequent forecasting performance. Four assimilation experiments were conducted to assess the performance of the full-hydrometeor assimilation scheme against the warm-rain assimilation scheme. These experiments also investigated reflectivity assimilation using both indirect and direct methods. We found that the nonlinearity of the radar operator in the two direct reflectivity assimilation experiments requires more iterations for cost function reduction than in the indirect assimilation method. The hydrometeor fields were reasonably analyzed using the full-hydrometeor assimilation scheme, particularly improving the simulation of ice-phase hydrometeors and reflectivity above the melting layer. The assimilation of radar reflectivity led to improvements in short-term (0–3 h) precipitation forecasting with the full-hydrometeor assimilation scheme. Assimilation experiments across multiple case studies reaffirmed that assimilating radar reflectivity observations with the full-hydrometeor assimilation scheme accelerated model spinup and yielded enhancements in 0–3-h total accumulate precipitation forecasts for a range of precipitation thresholds.

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Eva–Maria Walz
,
Peter Knippertz
,
Andreas H. Fink
,
Gregor Köhler
, and
Tilmann Gneiting

Abstract

Numerical weather prediction (NWP) models struggle to skillfully predict tropical precipitation occurrence and amount, calling for alternative approaches. For instance, it has been shown that fairly simple, purely data-driven logistic regression models for 24-hour precipitation occurrence outperform both climatological and NWP forecasts for the West African summer monsoon. More complex neural network based approaches, however, remain underdeveloped due to the non-Gaussian character of precipitation. In this study, we develop, apply and evaluate a novel two-stage approach, where we train a U-Net convolutional neural network (CNN) model on gridded rainfall data to obtain a deterministic forecast and then apply the recently developed, nonparametric Easy Uncertainty Quantification (EasyUQ) approach to convert it into a probabilistic forecast. We evaluate CNN+EasyUQ for one-day ahead 24-hour accumulated precipitation forecasts over northern tropical Africa for 2011–2019, with the Integrated Multi-satellitE Retrievals for GPM (IMERG) data serving as ground truth. In the most comprehensive assessment to date we compare CNN+EasyUQ to state-of-the-art physics-based and data-driven approaches such as a monthly probabilistic climatology, raw and postprocessed ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), and traditional statistical approaches that use up to 25 predictor variables from IMERG and the ERA5 reanalysis. Generally, statistical approaches perform about on par with postprocessed ECMWF ensemble forecasts. The CNN+EasyUQ approach, however, clearly outperforms all competitors for both occurrence and amount. Hybrid methods that merge CNN+EasyUQ and physics-based forecasts show slight further improvement. Thus, the CNN+EasyUQ approach can likely improve operational probabilistic forecasts of rainfall in the tropics, and potentially even beyond.

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Kuan-Yun Wang
,
Chung-Hsiung Sui
,
Mong-Ming Lu
, and
Jing-Shan Hong

Abstract

Episodic cold surges in the East Asia winter monsoon can penetrate deep into the South China Sea (SCS), enhance consequent tropical rainfall, and further strengthen the East Asia meridional overturning circulation. These cold surges can promote strong surface fluxes and lead to a deeper marine boundary layer (MBL). However, there is a lack of boundary layer studies over the SCS, unlike many other well-studied regions such as the north Atlantic Ocean and the central-eastern Pacific Ocean. In this study, we use high resolution radiosonde data of temperature and humidity profiles over Dongsha Island (116.69E, 20.70N) to identify the inversion layer, mixed layer, cloud base, cloud top, and factors controlling low cloud cover for the period of December-January-February from 2010 to 2020. We perform an energy budget analysis with ERA-5 meteorological variables and surface fluxes. Here we show a strong turbulent flux convergence of both heat and moisture within the SCS MBL during cold surges, which leads to a lifting of the mixed layer to ~1.0 km and inversion layer to ~2.0 km and associated cloud development over Dongsha Island. The cold and dry horizontal advection is balanced by this vertical turbulent flux convergence in the energy budget. Overall, cold surges over the SCS enhance lower branch of winter monsoon meridional overturning circulation with stronger inversion and higher low cloud covers.

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Yonghan Choi
,
Joowan Kim
,
Joo-Hong Kim
, and
Dong-Hyun Cha

Abstract

In this study, the effects of assimilating Global Navigation Satellite System (GNSS) radio occultation (RO) observations from existing and recently added commercial cube satellites on analyses and forecasts over the Arctic region were investigated by conducting observing system experiments (OSEs). Profiles of refractivity were assimilated with a local observation operator using the three-dimensional variational method. The analyses and forecasts from the OSEs were verified against ERA5 reanalysis, radiosonde observations, and buoy observations. In addition to the averaged impact on forecast skill, the impact of GNSS RO observations was further examined for an individual Arctic cyclone case, focusing on the added value of the cube satellite data. The effects of GNSS RO observations from existing satellites on analyses and forecasts over the Arctic region are positive, and the assimilation of GNSS RO observations from cube satellites leads to additional improvements, particularly for temperature in the upper troposphere and lower stratosphere (UTLS). Temperature biases in the UTLS are significantly reduced in the analyses, and the improved analyses result in better forecasts of upper-level potential vorticity and cyclone development when GNSS RO observations from cube satellites are assimilated. This result demonstrates the potential of GNSS RO data from cube satellites to enhance forecasts over the Arctic region.

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Geir Evensen
,
Femke C. Vossepoel
, and
Peter Jan van Leeuwen

Abstract

This paper identifies and explains particular differences and properties of adjoint-free iterative ensemble methods initially developed for parameter estimation in petroleum models. The aim is to demonstrate the methods’ potential for sequential data assimilation in coupled and multiscale unstable dynamical systems. For this study, we have introduced a new nonlinear and coupled multiscale model based on two Kuramoto–Sivashinsky equations operating on different scales where a coupling term relaxes the two model variables toward each other. This model provides a convenient testbed for studying data assimilation in highly nonlinear and coupled multiscale systems. We show that the model coupling leads to cross covariance between the two models’ variables, allowing for a combined update of both models. The measurements of one model’s variable will also influence the other and contribute to a more consistent estimate. Second, the new model allows us to examine the properties of iterative ensemble smoothers and assimilation updates over finite-length assimilation windows. We discuss the impact of varying the assimilation windows’ length relative to the model’s predictability time scale. Furthermore, we show that iterative ensemble smoothers significantly improve the solution’s accuracy compared to the standard ensemble Kalman filter update. Results and discussion provide an enhanced understanding of the ensemble methods’ potential implementation and use in operational weather- and climate-prediction systems.

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