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John Uehling, Vasubandhu Misra, Amit Bhardwaj, and Nirupam Karmakar

Abstract

In this study, we introduce a localized definition of the onset and retreat of the northern Australian rainy season that is solely based on gridded rainfall analysis. Our analysis shows that the local onset/retreat of the rainy season has considerable spatial heterogeneity. Onset is earlier and the length of the rainy season is longer to the west of the Gulf of Carpentaria than to its east. Furthermore, we also find the local onset/retreat is influenced by the wet and dry spells of the 30–60-day intraseasonal oscillation. Much of the retreat of the rainy season occurs in the dry phases of the intraseasonal oscillation. However, intriguingly, a majority of the local onset of the rainy season occurs during dry phases of the intraseasonal oscillation. The ENSO teleconnection with the variable-length northern Australian rainy season also exhibits spatial heterogeneity and significant differences from rainfall anomalies using the fixed-length boreal winter season. The onset, the retreat, the length, and the seasonal rainfall anomalies of the rainy season display a stronger correlation with the ENSO SST anomalies for the region east of 140°E relative to its west. The strong covariability of the local onset date with the corresponding seasonal length and seasonal rainfall anomalies over northern Australia offers the advantage of monitoring the onset of the northern Australian rainy season to provide an outlook for the forthcoming season. The proposed local definition of onset/retreat of the northern Australian rainy season is simple, objective, and unambiguous and is ideally suited for real-time monitoring of the evolution of the season.

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Xu Lu and Xuguang Wang

Abstract

Short-term spinup for strong storms is a known difficulty for the operational Hurricane Weather Research and Forecasting (HWRF) Model after assimilating high-resolution inner-core observations. Our previous study associated this short-term intensity prediction issue with the incompatibility between the HWRF Model and the data assimilation (DA) analysis. While improving physics and resolution of the model was found to be helpful, this study focuses on further improving the intensity predictions through the four-dimensional incremental analysis update (4DIAU). In the traditional 4DIAU, increments are predetermined by subtracting background forecasts from analyses. Such predetermined increments implicitly require linear evolution assumption during the update, which are hardly valid for rapidly evolving hurricanes. To confirm the hypothesis, a corresponding 4D analysis nudging (4DAN) method, which uses online increments is first compared with the 4DIAU in an oscillation model. Then, variants of 4DIAU are proposed to improve its application for nonlinear systems. Next, 4DIAU, 4DAN and their proposed improvements are implemented into the HWRF 4DEnVar DA system and are investigated with Hurricane Patricia (2015). Results from both the oscillation model and HWRF Model show that 1) the predetermined increments in 4DIAU can be detrimental when there are discrepancies between the updated and background forecasts during a nonlinear evolution; 2) 4DAN can improve the performance of incremental update upon 4DIAU, but its improvements are limited by the overfiltering; 3) relocating initial background before the incremental update can improve the corresponding traditional methods; and 4) the feature-relative 4DIAU method improves the incremental update the most and produces the best track and intensity predictions for Patricia among all experiments.

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Marie Mazoyer, Didier Ricard, Gwendal Rivière, Julien Delanoë, Philippe Arbogast, Benoit Vié, Christine Lac, Quitterie Cazenave, and Jacques Pelon

Abstract

This study investigates diabatic processes along the warm conveyor belt (WCB) of a deep extratropical cyclone observed in the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX). The aim is to investigate the effect of two different microphysics schemes, the one-moment scheme ICE3 and the quasi two-moment scheme LIMA, on the WCB and the ridge building downstream. ICE3 and LIMA also differ in the processes of vapor deposition on hydrometeors in cold and mixed-phase clouds. Latent heating in ICE3 is found to be dominated by deposition on ice while the heating in LIMA is distributed among depositions on ice, snow, and graupel. ICE3 is the scheme leading to the largest number of WCB trajectories (30% more than LIMA) due to greater heating rates over larger areas. The consequence is that the size of the upper-level ridge grows more rapidly in ICE3 than LIMA, albeit with some exceptions in localized regions of the cyclonic branch of the WCB. A comparison with various observations (airborne remote sensing measurements, dropsondes, and satellite data) is then performed. Below the melting layer, the observed reflectivity is rather well reproduced by the model. Above the melting layer, in the middle of the troposphere, the reflectivity and retrieved ice water content are largely underestimated by both schemes while at upper levels, the ICE3 scheme performs much better than LIMA in agreement with a closer representation of the observed winds by ICE3. These results underline the strong sensitivity of upper-level dynamics to ice-related processes.

Open access
Michael B. Natoli and Eric D. Maloney

Abstract

The impact of quasi-biweekly variability in the monsoon southwesterly winds on the precipitation diurnal cycle in the Philippines is examined using CMORPH precipitation, ERA5 data, and outgoing longwave radiation (OLR) fields. Both a case study during the 2018 Propagation of Intraseasonal Tropical Oscillations (PISTON) field campaign and a 23-yr composite analysis are used to understand the effect of the quasi-biweekly oscillation (QBWO) on the diurnal cycle. QBWO events in the west Pacific, identified with an extended EOF index, bring increases in moisture, cloudiness, and westerly winds to the Philippines. Such events are associated with significant variability in daily mean precipitation and the diurnal cycle. It is shown that the modulation of the diurnal cycle by the QBWO is remarkably similar to that by the boreal summer intraseasonal oscillation (BSISO). The diurnal cycle reaches maximum amplitude on the western side of the Philippines on days with average to above-average moisture, sufficient insolation, and weakly offshore prevailing wind. This occurs during the transition period from suppressed to active large-scale convection for both the QBWO and BSISO. Westerly monsoon surges associated with QBWO variability generally exhibit active precipitation over the South China Sea (SCS), but a depressed diurnal cycle. These results highlight that modes of large-scale convective variability in the tropics can have a similar impact on the diurnal cycle if they influence the local-scale environmental background state similarly.

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Rachel Gaal and James L. Kinter III

Abstract

Mesoscale convective systems (MCS) are known to develop under ideal conditions of temperature and humidity profiles and large-scale dynamic forcing. Recent work, however, has shown that summer MCS events can occur under weak synoptic forcing or even unfavorable large-scale environments. When baroclinic forcing is weak, convection may be triggered by anomalous conditions at the land surface. This work evaluates land surface conditions for summer MCS events forming in the U.S. Great Plains using an MCS database covering the contiguous United States east of the Rocky Mountains, in boreal summers 2004–16. After isolating MCS cases where synoptic-scale influences are not the main driver of development (i.e., only non-squall-line storms), antecedent soil moisture conditions are evaluated over two domain sizes (1.25° and 5° squares) centered on the mean position of the storm initiation. A negative correlation between soil moisture and MCS initiation is identified for the smaller domain, indicating that MCS events tend to be initiated over patches of anomalously dry soils of ~100-km scale, but not significantly so. For the larger domain, soil moisture heterogeneity, with anomalously dry soils (anomalously wet soils) located northeast (southwest) of the initiation point, is associated with MCS initiation. This finding is similar to previous results in the Sahel and Europe that suggest that induced meso-β circulations from surface heterogeneity can drive convection initiation.

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Y. Dai and S. Hemri

Abstract

Statistical postprocessing is commonly applied to reduce location and dispersion errors of probabilistic forecasts provided by numerical weather prediction (NWP) models. If postprocessed forecast scenarios are required, the combination of ensemble model output statistics (EMOS) for univariate postprocessing with ensemble copula coupling (ECC) or the Schaake shuffle (ScS) to retain the dependence structure of the raw ensemble is a state-of-the-art approach. However, modern machine learning methods may lead to both a better univariate skill and more realistic forecast scenarios. In this study, we postprocess multimodel ensemble forecasts of cloud cover over Switzerland provided by COSMO-E and ECMWF-IFS using (i) EMOS + ECC, (ii) EMOS + ScS, (iii) dense neural networks (dense NN) + ECC, (iv) dense NN + ScS, and (v) conditional generative adversarial networks (cGAN). The different methods are verified using EUMETSAT satellite data. Dense NN shows the best univariate skill, but cGAN performed only slightly worse. Furthermore, cGAN generates realistic forecast scenario maps, while not relying on a dependence template like ECC or ScS, which is particularly favorable in the case of complex topography.

Open access
Fabienne Schmid, Elena Gagarina, Rupert Klein, and Ulrich Achatz

Abstract

Idealized integral studies of the dynamics of atmospheric inertia–gravity waves (IGWs) from their sources in the troposphere (e.g., by spontaneous emission from jets and fronts) to dissipation and mean flow effects at higher altitudes could contribute to a better treatment of these processes in IGW parameterizations in numerical weather prediction and climate simulation. It seems important that numerical codes applied for this purpose are efficient and focus on the essentials. Therefore, a previously published staggered-grid solver for f-plane soundproof pseudoincompressible dynamics is extended here by two main components. These are 1) a semi-implicit time stepping scheme for the integration of buoyancy and Coriolis effects, and 2) the incorporation of Newtonian heating consistent with pseudoincompressible dynamics. This heating function is used to enforce a temperature profile that is baroclinically unstable in the troposphere and it allows the background state to vary in time. Numerical experiments for several benchmarks are compared against a buoyancy/Coriolis-explicit third-order Runge–Kutta scheme, verifying the accuracy and efficiency of the scheme. Preliminary mesoscale simulations with baroclinic wave activity in the troposphere show intensive small-scale wave activity at high altitudes, and they also indicate there the expected reversal of the zonal-mean zonal winds.

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Paul J. Roebber

Abstract

We introduce an adaptive form of postprocessor where algorithm structures are neural networks where the number of hidden nodes and the network training features evolve. Key potential advantages of this system are the flexible, nonlinear mapping capabilities of neural networks and, through backpropagation, the ability to rapidly establish capable predictors in an algorithm population. The system can be implemented after one initial training process and future changes to postprocessor inputs (new observations, new inputs, or model upgrades) are incorporated as they become available. As in prior work, the implementation in the form of a predator–prey ecosystem allows for the ready construction of ensembles. Computational requirements are minimal, and the use of a moving data window means that data storage requirements are constrained. The system adds predictive skill to a demonstration dynamical model representing the hemispheric circulation, with skill competitive with or exceeding that obtainable from multiple linear regression and standard artificial neural networks constructed under typical operational limitations. The system incorporates new information rapidly and the dependence of the approach on the training data size is similar to multiple linear regression. A loss of performance occurs relative to a fixed neural network architecture in which only the weights are adjusted after training, but this loss is compensated for by gains from the ensemble predictions. While the demonstration dynamical model is complex, current numerical weather prediction models are considerably more so, and thus a future step will be to apply this technique to operational weather forecast data.

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Ryan Lagerquist, Jebb Q. Stewart, Imme Ebert-Uphoff, and Christina Kumler

Abstract

Predicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 min. The goal is to make skillful forecasts with only present and past satellite data as predictors. Specifically, predictors are multispectral brightness-temperature images from the Himawari-8 satellite, while targets (ground truth) are provided by weather radars in Taiwan. U-nets are becoming popular in atmospheric science due to their advantages for gridded prediction. Furthermore, we use three novel approaches to advance U-nets in atmospheric science. First, we compare three architectures—vanilla, temporal, and U-net++—and find that vanilla U-nets are best for this task. Second, we train U-nets with the fractions skill score, which is spatially aware, as the loss function. Third, because we do not have adequate ground truth over the full Himawari-8 domain, we train the U-nets with small radar-centered patches, then apply trained U-nets to the full domain. Also, we find that the best predictions are given by U-nets trained with satellite data from multiple lag times, not only the present. We evaluate U-nets in detail—by time of day, month, and geographic location—and compare them to persistence models. The U-nets outperform persistence at lead times ≥ 60 min, and at all lead times the U-nets provide a more realistic climatology than persistence. Our code is available publicly.

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Kyle Ahern, Robert E. Hart, and Mark A. Bourassa

Abstract

In this first part of a two-part study, the three-dimensional structure of the inner-core boundary layer (BL) is investigated in a full-physics simulation of Hurricane Irma (2017). The BL structure is highlighted during periods of intensity change, with focus on features and mechanisms associated with storm decay. The azimuthal structure of the BL is shown to be linked to the vertical wind shear and storm motion. The BL inflow becomes more asymmetric under increased shear. As BL inflow asymmetry amplifies, asymmetries in the low-level primary circulation and thermodynamic structure develop. A mechanism is identified to explain the onset of pronounced structural asymmetries in coincidence with external forcing (e.g., through shear) that would amplify BL inflow along limited azimuth. The mechanism assumes enhanced advection of absolute angular momentum along the path of the amplified inflow (e.g., amplified downshear), which results in local spinup of the vortex and development of strong supergradient flow downwind and along the BL top. The associated agradient force results in the outward acceleration of air immediately above the BL inflow, affecting fields including divergence, vertical motion, entropy advection, and inertial stability. In this simulation, descending inflow in coincidence with amplified shear is identified as the conduit through which low-entropy air enters the inner-core BL, thereby hampering convection downwind and resulting in storm decay.

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