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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
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
Clayton R. S. Sasaki
,
Angela K. Rowe
,
Lynn A. McMurdie
,
Adam C. Varble
, and
Zhixiao Zhang

Abstract

This study documents the spatial and temporal distribution of the South American low-level jet (SALLJ) and quantifies its impact on the convective environment using a 6.5-month convection-permitting simulation during the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) campaigns. Overall, the simulation reproduces the observed SALLJ characteristics in central Argentina near the Sierras de Córdoba (SDC), a focal point for terrain-focused upscale growth. SALLJs most frequently occur in the summer with maxima to the northwest and east of the SDC and minima over the higher terrain. The shallower SALLJs (<1750 m) have a strong overnight skew, while the elevated jets are more equally spread throughout the day. SALLJ periods often have higher amounts of low-level moisture and instability compared to non-SALLJ periods, with these impacts increasing over time when the SALLJ is present and decreasing afterward. The SALLJ may enhance low-level wind shear magnitudes (particularly when accounting for the jet height); however, enhancement is somewhat limited due to the presence of speed shear in most situations. SALLJ periods are associated with low-level directional shear favorable for organized convection and an orientation of cloud-layer wind shear parallel to the terrain, which could favor upscale growth. A case study is shown in which the SALLJ influenced both the magnitude and direction of wind shear concurrent with convective upscale growth near the SDC. This study highlights the complex relationship between the SALLJ and its impacts during periods of widespread convection.

Significance Statement

Areas of enhanced low-level winds, or low-level jets, likely promote favorable conditions for upscale growth, the processes by which storms grow larger. Central Argentina is an ideal place to study the influence of low-level jets on upscale growth as storms often stay connected to the Sierras de Córdoba Mountain range, growing over a relatively small area. This study uses model data to describe the distribution and impact of the South American low-level jet on the storm environment. The South American low-level jet is frequently found near the Sierras de Córdoba, and moisture and convective instability increase when it is present. However, the jet’s impact on other conditions important for upscale growth, such as vertical wind shear, is not as straightforward.

Open access
Feimin Zhang
,
Kaixuan Bi
,
Sentao Wei
, and
Chenghai Wang

Abstract

This study investigates the influences of initial soil moisture over the Tibetan Plateau (TP) on precipitation simulation, and the respective effects of boundary layer vertical diffusion for heat (Kh ) and vapor (Kq ). Results indicate that the responses of boundary layer vertical diffusion to soil moisture are obvious mainly in the daytime. Wetter land surface corresponds to weaker vertical diffusion, which could strengthen thermal forcing and dynamic lifting in the lower atmosphere, and encourage water vapor saturation near the top of boundary layer to prevent the environmental dry air entrainment/invasion, which would be beneficial to more convection and precipitation. Wetter land surface over the TP could enhance the contrast between the cold in the northwestern TP and the warm in the southeastern TP, which would be conducive to the southeastward propagation of precipitation. The simulation of heat and moisture in the boundary layer could be improved by perturbing the relative intensity of Kh and Kq . From the perspective of heat and moisture, Kh affects atmospheric stability, while Kq affects moisture and its vertical transport in the boundary layer. The Kh and Kq have competitive effects on precipitation intensity by influencing the relative importance of moisture and atmospheric stability conditions in the boundary layer. Adjusting the relative intensity of Kh and Kq would deactivate the competitive effects. Stronger Kh but weaker Kq would alleviate the overestimated precipitation by inhibiting vertical transport of moisture to the top of boundary layer and attenuating convective instability in the boundary layer.

Significance Statement

The purpose of this study is to better understand the effects of boundary layer vertical heat and moisture diffusion in the response of precipitation to soil moisture. This is important because boundary layer vertical diffusion is a crucial factor influencing the relation between soil moisture and precipitation. Our results reveal the competitive effects of boundary layer vertical diffusion for heat and vapor on the simulation of precipitation. These results point a potential way toward better understanding the response of precipitation to soil moisture.

Open access
Grant LaChat
,
Kevin A. Bowley
, and
Melissa Gervais

Abstract

Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. Traditionally, the type of breaking is categorized as anticyclonic (AWB) or cyclonic (CWB) and can be identified using the orientation of streamers of high potential temperature (θ) and low θ air on a PV surface. However, an examination of the differences in RWB structure and their associated tropospheric impacts within these types remains unexplored. In this study, AWB and CWB are identified from overturning isentropes on the dynamic tropopause (DT), defined as the 2 potential vorticity unit (PVU; 1 PVU = 10−6 K kg−1 m2 s−1) surface, in the ERA5 dataset during December, January, and February 1979–2019. Self-organizing maps (SOM), a machine learning method, is used to cluster the identified RWB events into archetypal patterns, or “flavors,” for each type. AWB and CWB flavors capture variations in the θ minima/maxima of each streamer and the localized meridional θ gradient (∇θ) flanking the streamers. Variations in the magnitude and position of ∇θ between flavors correspond to a diversity of jet structures leading to differences in vertical motion patterns and troposphere-deep circulations. A subset of flavors of AWB (CWB) events are associated with the development of strong surface high (low) pressure systems and the generation of extreme poleward moisture transport. For CWB, many events occurred in similar geographical regions, but the precipitation and moisture patterns were vastly different between flavors. Our findings suggest that the location, type, and severity of the tropospheric impacts from RWB are strongly dictated by RWB flavor.

Significance Statement

Large-scale atmospheric waves ∼15 km above Earth’s surface are responsible for the daily weather patterns that we experience. These waves can undergo wave breaking, a process that is analogous to ocean waves breaking along the seashore. Wave breaking events have been linked to extreme weather impacts at the surface including cold and heat waves, strong low pressure systems, and extreme precipitation events. Machine learning is used to identify and analyze different flavors, or patterns, of wave breaking events that result in differing surface weather impacts. Some flavors are able to generate notable channels of moisture that result in extreme high precipitation events. This is a crucial insight as forecasting of extreme weather events could be improved from this work.

Open access
Monika Feldmann
,
Richard Rotunno
,
Urs Germann
, and
Alexis Berne

Abstract

This study investigates the effects of lakes in mountainous terrain on the evolution of supercell thunderstorms. With a newly developed radar-based, mesocyclone-detection algorithm, a recent study has characterized the occurrence and evolution of supercell thunderstorms in the Swiss Alpine region. That study highlights the influence of orography on both storm intensity and occurrence frequency. To disentangle the different influential factors, an idealized modeling framework is established here using the mesoscale model CM1. The modeling scenarios are based on a high-CAPE environment with unidirectional shear, where a warm bubble serves to initiate the convection. Mimicking the environment of the southern Prealps in central Europe, scenarios with a high mountain ridge, valleys, and lakes are explored. The effect on the supercells of the slopes, high-altitude terrain, and moisture sources emphasizes the highly localized nature of terrain effects, leading to a heterogeneous intensity life cycle with transitory enhancement and weakening of the supercell. The dynamic and thermodynamic impact of mountain valleys with lakes increases the range of atmospheric conditions that supports supercellular development through horizontal vorticity production, increased storm relative helicity, and higher moisture content. This influence results in a systematic location dependence of the frequency, intensity, and lifetime of supercells, as also found in observations.

Open access
Xubin Zhang
and
Jingshan Li

Abstract

In this study, downscaling, ensemble data assimilation, time lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–20. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. The combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. The combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.

Significance Statement

Precipitation forecasting for heavy-rainfall events over South China in the rainy season is still challenging due to large uncertainties. Convection-permitting ensemble forecasting is expected to address such uncertainties to improve forecasts of heavy rainfall. However, it is not yet clear how to optimally design convection-permitting ensembles by implementing perturbations in initial conditions (ICs). This study investigates the impacts of various IC perturbation methods on convection-permitting ensemble forecasting over South China in the rainy season. Various IC perturbation methods show discrepant multiscale characteristics of perturbations, which generally complement each other when these perturbations are combined. The added values of combining various IC perturbation methods in forecasting are confirmed for most variables. However, such values are case dependent, with the largest values for tropical cyclone cases during the early forecasts and for the presummer rainy season cases with strong synoptic-scale forcing during late forecasts. Thus, it is still essential to further improve the combination of various types of IC perturbation methods.

Open access
Julian O’Grady
,
Hamish Ramsay
,
Kathleen McInnes
, and
Rebecca Gregory

Abstract

Hazard studies based on thousands of synthetic tropical cyclone (TC) events require a validated model representation of the surface wind field. Here, we assess three different TC parametric vortex models with input from four along-track parameter studies of the TC size and shape, based on statistical formulation of the relationships to observed TC intensity, geographic location, and forward transition speed. The 12 model combinations are compared to in situ 10-min observed surface mean wind speeds for 10 TCs that made landfall over Queensland, Australia, which occurred over the period 2006–17. Empirical wind reduction factors to reduce gradient winds to the surface are recalculated for the more recent TCs at both offshore (ocean, small islands, reefs, and moorings) and onshore (land) locations. To improve the wind comparisons over ocean and land, a secondary reduction factor was developed based on an inland decay function. Pearson correlations for the unadjusted modeled peak wind speed from 118 instances of a TC passing a weather station sit between a range of 0.57 and 0.65 for the 12 model combinations. Using the secondary reduction factor based on the inland decay function increases the range of correlation to 0.74–0.81. Based on the assessment of the instances of peak surface wind speed correlations, bias, and root-mean-square error, along with the correlation 48 h around the peak, the top-ranked performing model combination for the region was an along-track parameter study with a double-vortex model, both previously tested for the South Pacific basin.

Significance Statement

When assessing tropical cyclone hazards, users are presented with several simplified parametric models to describe the surface wind field of tropical cyclones. These parametric models are used routinely for risk assessment of cyclonic winds, as well as for input to surge and wave models used in coastal hazard assessments. Differences between the models include the formulation of the parametric cyclone model, the way winds above the boundary layer are specified at the surface and along-track parameters that describe the cyclones’ size and shape. Of the 12 model combinations investigated in this study, the top-ranked performing model combination for the region was an along-track parameter equation with a double-vortex model, which were both tested previously for the South Pacific basin. Analysis is performed to show unadjusted modeled winds overestimate observed 10-min surface winds over the ocean by around 13% (median) and over land by around 73.9% (median), which is resolved in this study with a secondary empirical wind reduction factor. These findings will support future modeling of tropical cyclone winds for multiple applications, including regional risk assessment and coastal hazard studies.

Open access
Nicolas Bruneau
,
Thomas Loridan
,
Nic Hannah
,
Eugene Dubossarsky
,
Mathis Joffrain
, and
John Knaff

Abstract

While tropical cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely, the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e., “catastrophe models”).

Open access