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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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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
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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Peyton K. Capute and Ryan D. Torn

Abstract

Arctic cyclones (ACs) are synoptic-scale features that can be associated with strong, intense winds over the Arctic Ocean region for long periods of time, potentially leading to rapid declines of sea ice during the summer. As a consequence, sea ice predictions may rely on the predictability of cyclone-related wind speed and direction, which critically depends on the cyclone track and intensity. Despite this, there are relatively few studies that have documented the predictability of ACs during the summer, beyond a few case studies, nor has there been an extensive comparison of whether these cyclones are more or less predictable relative to comparable midlatitude cyclones, which have been studied in greater detail. The goal of this study is to document the practical predictability of AC position and intensity forecasts over 100 cases and compare it with 89 Atlantic Ocean basin midlatitude cyclones using the Global Ensemble Forecast System (GEFS) Reforecast V2. This dataset contains 11-member ensemble forecasts initialized daily from 1985 to the present using a fixed model. In this study, forecasts initialized 1 and 3 days prior to the cyclone development time are compared, where predictability is defined as the ensemble mean root-mean-square error and ensemble standard deviation (SD). Although Atlantic basin cyclone tracks are characterized by higher predictability relative to comparable ACs, intensity predictability is higher for ACs. In addition, storms characterized by low ensemble SD and predictability are found in regions of higher baroclinic instability than storms characterized by high predictability. There appears to be little, if any, relationship between latent heat release and precipitable water and predictability.

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Ken Sawada and Yuki Honda

Abstract

The reproducibility of precipitation in the early stages of forecasts, often called a spindown or spinup problem, has been a significant issue in numerical weather prediction. This problem is caused by moisture imbalance in the analysis data, and in the case of the Japan Meteorological Agency’s (JMA’s) mesoscale data assimilation system, JNoVA, we found that the imbalance stems from the existence of unrealistic supersaturated states in the minimal solution of the cost function in JNoVA. Based on the theory of constrained optimization problems, we implemented an exterior penalty function method for the mixing ratio within JNoVA to suppress unrealistic supersaturated states. The advantage of this method is the simplicity of its theory and implementation. The results of twin data assimilation cycle experiments conducted for the heavy rain event of July 2018 over Japan show that—with the new method—unrealistic supersaturated states are reduced successfully, negative temperature bias to the observations is alleviated, and a sharper distribution of the mixing ratio is obtained. These changes help to initiate the development of convection at the proper location and improve the fractions skill score (FSS) of precipitation in the early stages of the forecast. From these results, we conclude that the initial shock caused by moisture imbalance is mitigated by implementing the penalty function method, and the new moisture balance has a positive impact on the reproducibility of precipitation in the early stages of forecasts.

Open access
A. C. Sousa, L. A. Candido, and P. Satyamurty

Abstract

Mesoscale convective cloud clusters develop and organize in the form of squall lines along the coastal Amazon in the afternoon hours and propagate inland during the evening hours. The frequency, location, organization into lines, and movement of the convective systems are determined by analyzing the “precipitation features” obtained from the TRMM satellite for the period 1998–2014. The convective clusters and their alignments into Amazon coastal squall lines are more frequent from December to July, and they mostly stay within 170 km of the coastline. Their development and movement in the afternoon and evening hours of about 14 m s−1 are helped by the sea breeze. Negative phase of Atlantic dipole and La Niña combined increase the frequency of convective clusters over the coastal Amazon. Composite environmental conditions of 13 large Amazon coastal squall-line cases in April show that conditional instability increases from 0900 to 1200 LT and the wind profiles show a jet-like structure at low levels of the atmosphere. The differences in the vertical profiles of temperature and humidity between the large-squall-line composites and no-squall-line composites are weak. However, appreciable increase in the mean value of CAPE from 0900 to 1500 LT is found in the large-squall-line composite. The mean mixing ratio of the mixed layer at 0900 LT in La Niña situations is significantly larger in the large-squall-line composite. Thus, CAPE and mixed-layer mixing ratio are considered to be promising indicators of the convective activity over the coastal belt of the Amazon basin.

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S. K. Mishra

Abstract

Structure and time evolution of the large-scale background and an embedded synoptic-scale monsoon depression and their interactions are studied. The depression formation is preceded by a cyclonic circulation around 400 hPa. The Fourier-based scale separation technique is used to isolate large (wavenumbers 0–8) and synoptic-scale (wavenumbers 12–60). The wavelength and depression center is determined objectively. The synoptic-scale depression has an average longitudinal wavelength of around 1900 km and a north–south size of 1100 km; it is most intense with a vorticity of 20.5 × 10−5 s−1 at 900 hPa. The strongest cold core of −3.0°C below 850 hPa and the above warm core of around 2.0°C are evident. The depression is tilted southwestward in the midtroposphere with no significant vertical tilt in the lower troposphere. The mean maximum intensity and upward motion over the life cycle of depression are in close agreement with the composite values. A strong cyclonic shear zone is developed in the midtroposphere preceding the depression. The necessary condition for barotropic (baroclinic) instability is satisfied in the midtroposphere (boundary layer). Strong northward transport of momentum by the depression against the southward shear is found. The strong growth of the MD in the lower troposphere is due to downward transfer of excess energy gained in the midtroposphere from the barotropic energy conversion and east–west direct thermal circulation as the vertical energy flux. The baroclinic interaction contributes to the maintenance of the cold core in the lower troposphere. The diabatic heating rate is computed and its role in the genesis and growth of MD is investigated.

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Chanil Park, Seok-Woo Son, Joowan Kim, Eun-Chul Chang, Jung-Hoon Kim, Enoch Jo, Dong-Hyun Cha, and Sujong Jeong

Abstract

This study identifies diverse synoptic weather patterns of warm-season heavy rainfall events (HREs) in South Korea. The HREs not directly connected to tropical cyclones (TCs) (81.1%) are typically associated with a midlatitude cyclone from eastern China, the expanded North Pacific high, and strong southwesterly moisture transport in between. They are frequent both in the first (early summer) and second rainy periods (late summer) with impacts on the south coast and west of the mountainous region. In contrast, the HREs resulting from TCs (18.9%) are caused by the synergetic interaction between the TC and meandering midlatitude flow, especially in the second rainy period. The strong south-southeasterly moisture transport makes the southern and eastern coastal regions prone to the TC-driven HREs. By applying a self-organizing map algorithm to the non-TC HREs, their surface weather patterns are further classified into six clusters. Clusters 1 and 3 exhibit a frontal boundary between the low and high with differing relative strengths. Clusters 2 and 5 feature an extratropical cyclone migrating from eastern China under different background sea level pressure patterns. Cluster 4 is characterized by the expanded North Pacific high with no organized negative sea level pressure anomaly, and cluster 6 displays a development of a moisture pathway between the continental and oceanic highs. Each cluster exhibits a distinct spatiotemporal occurrence distribution. The result provides useful guidance for HRE prediction by depicting important factors to be differently considered depending on their synoptic categorization.

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Rama Sesha Sridhar Mantripragada, C. J. Schreck III, and Anantha Aiyyer

Abstract

Perturbation kinetic and available energy budgets are used to explore how convectively coupled equatorial Kelvin waves (KWs) impact African easterly wave (AEW) activity. The convective phase of the Kelvin wave increases the African easterly jet’s meridional shear, thus enhancing the barotropic energy conversions, leading to intensification of southern track AEWs perturbation kinetic energy. In contrast, the barotropic energy conversion is reduced in the suppressed phase of KW. Baroclinic energy conversion of the southern track AEWs is not significantly different between Kelvin waves’ convective and suppressed phases. AEWs in the convective phase of a Kelvin wave have stronger perturbation available potential energy generation by diabatic heating and stronger baroclinic overturning circulations than in the suppressed phase of a Kelvin wave. These differences suggest that southern track AEWs within the convective phase of Kelvin waves have more vigorous convection than in the suppressed phase of Kelvin waves. Barotropic energy conversion of the northern track AEWs is not significantly different between Kelvin waves’ convective and suppressed phases. The convective phase of the Kelvin wave increases the lower-tropospheric meridional temperature gradient north of the African easterly jet, thus enhancing the baroclinic energy conversion, leading to intensification of northern track AEWs perturbation kinetic energy. In contrast, the baroclinic energy conversion is reduced in the suppressed phase of KW. These results provide a physical basis for the modulation of AEWs by Kelvin waves arriving from upstream.

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