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Lu Yang
,
Linye Song
,
Mingxuan Chen
, and
Conglan Cheng

Abstract

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is firstly to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Secondly, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial-temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it against the observations. Results indicate that: (1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. (2) The PDF curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. (3) The NPS (Nowcasting Prediction System) analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement

The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model simulation gusts.

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Ricardo C. Muñoz
and
Laurence Armi

Abstract

Raco is a local wind occurring in central Chile where the Maipo River Canyon exits into the Santiago valley. The intensification of the easterly down-canyon flow starts any time during some cold season nights, accompanied by increases in temperature and drops in humidity. The hypothesis of the raco being a gap wind controlled by the narrowest section in the 12-km canyon exit corridor is tested with data from two events in July 2018 and July 2019. The data are analyzed in the framework of hydraulic theory and a subcritical-to-supercritical transition is documented to occur at the narrows of the gap where the Froude number is close to unity, confirmed by radiosondes launched in the narrows in 2019. For the raco flow, the sum of potential and kinetic energy is conserved upstream of the narrows, while the acceleration occurring farther downstream loses a large fraction of energy to frictional dissipation. The raco events occur under the influence of regional subsidence, but a differential nocturnal warming of the in-canyon airmass is responsible for a pressure gradient driving the raco. In the 2019 case, a ceilometer mounted on an instrumented pickup truck documented the structure and movement of the interface between the raco air and the cold-air pool (CAP) existing over the valley to the west. Together with a radiosonde launched near the CAP-raco surface front, the observations reveal the intense shear-driven mixing taking place at the interface and the factors supporting the establishment of a stationary front.

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Yoonjin Lee
and
Kyle Hilburn

Abstract

Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN’s accuracy against the Multi-Radar/Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day-of-year, and time-of-day. Since GREMLIN was trained with data in spring and summer, root-mean square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD due to cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington state, lower 30 dBZ critical success index due to high misses over the Northeast, and higher false alarms over Florida due to higher frequency of lightning.

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Sudheer R. Bhimireddy
and
David A. R. Kristovich

Abstract

This study evaluates the methods of identifying the height zi of the top of the convective boundary layer (CBL) during winter (December and January) over the Great Lakes and nearby land areas using observations taken by the University of Wyoming King Air research aircraft during the Lake-Induced Convection Experiment (1997/98) and Ontario Winter Lake-effect Systems (2013/14) field campaigns. Since CBLs facilitate vertical mixing near the surface, the most direct measurement of zi is that above which the vertical velocity turbulent fluctuations are weak or absent. Thus, we use zi from the turbulence method as the “reference value” to which zi from other methods, based on bulk Richardson number (Ri b ), liquid water content, and vertical gradients of potential temperature, relative humidity, and water vapor mixing ratio, are compared. The potential temperature gradient method using a threshold value of 0.015 K m−1 for soundings over land and 0.011 K m−1 for soundings over lake provided the estimates of zi that are most consistent with the turbulence method. The Ri b threshold-based method, commonly used in numerical simulation studies, underestimated zi . Analyzing the methods’ performance on the averaging window z avg we recommend using z avg = 20 or 50 m for zi estimations for lake-effect boundary layers. The present dataset consists of both cloudy and cloud-free boundary layers, some having decoupled boundary layers above the inversion top. Because cases of decoupled boundary layers appear to be formed by nearby synoptic storms, we recommend use of the more general term, elevated mixed layers.

Significance Statement

The depth zi of the convective atmospheric boundary layer (CBL) strongly influences precipitation rates during lake-effect snowstorms (LES). However, various zi approximation methods produce significantly different results. This study utilizes extensive concurrently collected observations by project aircraft during two LES field studies [Lake-Induced Convection Experiment (Lake-ICE) and OWLeS] to assess how zi from common estimation methods compare with “reference” zi derived from turbulent fluctuations, a direct measure of CBL mixing. For soundings taken both over land and lake; with cloudy or cloud-free conditions, potential temperature gradient (PTG) methods provided the best agreement with the reference zi . A method commonly employed in numerical simulations performed relatively poorly. Interestingly, the PTG method worked equally well for “coupled” and elevated decoupled CBLs, commonly associated with nearby cyclones.

Open access
Isaiah Kingsberry
and
Jason Naylor

Abstract

This study examines ground-based precipitation observations recorded by a high-density gauge network located within approximately 40 km of the urban center of Louisville, Kentucky. An analysis of April–October events reveals that precipitation is significantly greater on the downwind side of Louisville than on the upwind side, particularly when precipitation systems have a westerly component to their motion. The mean difference between downwind and upwind precipitation across all events is 20%. This value is smaller for widespread precipitation events (i.e., most or all gauges detect precipitation) and is larger for isolated events (i.e., rain detected by one-half of the gauges or fewer). The largest and most significant differences between upwind and downwind precipitation amounts occur in association with moist moderate, moist tropical, and transitional air masses.

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Hiroyuki Kusaka
,
Yuma Imai
,
Hiroki Kobayashi
,
Quang-Van Doan
, and
Thanh Ngo-Duc

Abstract

North-Central Vietnam often experiences high temperatures. Foehn winds originating from the Truong Son Mountains (also known as Laos winds) are believed to contribute to abnormally high temperatures; however, no quantitative research has focused on foehn warming in Vietnam. In this study, we conducted numerical simulations using the Weather Research and Forecasting (WRF) model to investigate the contribution of foehn warming to abnormally high temperatures in north-central Vietnam in early June 2017. Generally, May–June is the monsoon period in Vietnam. Consequently, foehn warming during this season is thought to be mainly caused by latent heating and precipitation mechanism. However, the primary factor in the cases covered in this study was foehn warming with an isentropic drawdown mechanism. Diabatic heating with turbulent diffusion and sensible heat flux from mountain slopes also play significant roles. The warming effect of the foehn winds on the temperatures during the events was approximately 2–3°C. It was concluded that the high temperature events from May 31-June 5, 2017 were caused by synoptic-scale warm advection and foehn warming. Sensitivity experiments were conducted on the WRF model, utilizing three atmospheric boundary layer turbulence schemes (YSU, ACM2, and MYNN), consistently yielding results for simulated temperature and relative humidity. The wind speed bias for the MYNN scheme was found to be lower than that of the other schemes. However, this study did not delve into the underlying reasons for these differences. The optimal performance of each scheme remains an open question.

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Shengjun Liu
,
Wenjie Yan
,
Xinru Liu
,
Yamin Hu
, and
Dangfu Yang

Abstract

The research and application of convolutional neural networks (CNNs) on statistical downscaling have been hampered by the fact that deep learning is highly dependent on sample size and is considered to be a black-box model. Therefore, a CNN model with transfer learning (CNN-TL) is proposed to study the pre-rainy season precipitation of South China. First, an augmented monthly dataset is created by sliding a fixed-length window over the daily circulation field and precipitation data for the entire year. Next, a base CNN network is pretrained on the augmented dataset, and then the network parameters are tuned on the actual monthly dataset from South China. Then, guided backpropagation is conducted to obtain the distribution regions of the key features and explain the net. The coefficient of determination R 2 and root-mean-square error (RMSE) show that the CNN-TL model has higher explanatory power and better fitting performance than the feature extraction–based random forest. In comparison with the base CNN, the transfer learning approach can improve the explanatory power of the model by 10.29% and reduce the average RMSE by 6.82%. In addition, the interpretation results of the model show that the critical regions are primarily South China and its surrounding areas, including the Indochina Peninsula, the Bay of Bengal, and the South China Sea. Furthermore, the ablation experiments and composite analysis illustrate that these regions are very important.

Significance Statement

To mitigate the challenges posed by small sample sizes and the transparency of deep learning in downscaling problems, we propose a convolutional neural network based on sample augmentation and transfer learning to study the monthly precipitation downscaling problem during the preflood period in South China. In comparison with random forests and conventional convolutional neural networks, our model achieves an optimal interpretation rate and stability. In addition, we explore the interpretability of the model using guided backpropagation to find the distribution of key features within the large-scale circulation field, thus increasing the credibility of the model.

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Khadija Arjdal
,
Étienne Vignon
,
Fatima Driouech
,
Frédérique Chéruy
,
Salah Er-Raki
,
Adriana Sima
,
Abdelghani Chehbouni
, and
Philippe Drobinski

Abstract

Land surface–atmosphere interactions are a key component of climate modeling. They are particularly critical to understand and anticipate the climate and the water resources over the semiarid and arid North African regions. This study uses in situ observations to assess the ability of the IPSL-CM global climate model to simulate the land–atmosphere interactions over the Moroccan semiarid plains. A specific configuration with a grid refinement over the Haouz Plain, near Marrakech, and nudging outside Morocco has been performed to properly assess the model’s performances. To ensure reliable model–observation comparisons despite the fact that station measurements are not representative of a mesh-size area, we carried out experiments with adapted vegetation properties. Results show that the CMIP6 version of the model’s physics represents the near-surface climate over the Haouz Plain reasonably well. Nonetheless, the simulation exhibits a nocturnal warm bias, and the wind speed is overestimated in tree-covered meshes and underestimated in the wheat-covered region. Further sensitivity experiments reveal that LAI-dependent parameterization of roughness length leads to a strong surface wind drag and to underestimated land surface atmosphere thermal coupling. Setting the roughness heights to the observed values improves the wind speed and, to a lesser extent, the nocturnal temperature. A low bias in latent heat flux and soil moisture coinciding with a pronounced diurnal warm bias at the surface is still present in our simulations. Including a first-order irrigation parameterization yields more realistic simulated evapotranspiration flux and daytime skin surface temperatures. This result raises the importance of accounting for the irrigation process in present and future climate simulations over Moroccan agricultural areas.

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Harrison Woodson Bowles
and
Sarah E. Strazzo

Abstract

Florida’s summertime precipitation patterns are in part influenced by convergence between the synoptic-scale wind and local sea-breeze fronts that form along the east and west coasts of the peninsula. While the National Weather Service previously defined nine sea-breeze regimes resulting from variations in the synoptic-scale vector wind field near Tampa, Florida, these regimes were developed using a shorter 18-yr period and examined primarily for the purposes of short-term weather prediction. This study employs reanalysis data to develop a full 30-yr climatology of the Florida sea-breeze regime distribution and analyze the composite mean atmospheric conditions associated with each regime. Further, given that 1) the synoptic-scale wind primarily varies as a result of movement in the western ridge of the North Atlantic subtropical high (NASH), and 2) previous studies suggest long-term shifts in the mean position of the NASH western ridge, this study also examines variability and trends in the sea-breeze regime distribution and its relationship to rainy-day frequency over a longer 60-yr period. Results indicate that synoptic-scale flow from the west through southwest, which enhances precipitation probabilities along the eastern half of the peninsula, has increased in frequency, while flow from the east through northeast has decreased in frequency. These changes in the sea-breeze regime distribution may be partially responsible for increases in rainy-day frequency during June–August over northeastern Florida, though results suggest that other factors likely contribute to interannual variability in precipitation across the southern peninsula.

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