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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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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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C. Cammalleri
,
N. McCormick
,
J. Spinoni
, and
J. W. Nielsen-Gammon

Abstract

The standardized precipitation index (SPI) is the most commonly used index for detecting and characterizing meteorological droughts, and it is also extensively used as a proxy variable for soil moisture anomalies (SMA) for the purpose of monitoring agricultural drought in absence of long-term soil moisture observations. However, the potential capability of SPI to warn of the time-lagged soil water deficit—following the well-known “drought cascade” effect—is often overlooked in agricultural drought studies. In this research, a time-lagged correlation analysis is used to evaluate the relationship between the SMA dataset, generated as part of the Global Drought Observatory of the European Union’s Copernicus Emergency Management Service, and a set of SPIs derived from the ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts. The possibility to achieve an optimal agreement between SPI and SMA that also preserves the early warning skills of SPI is evaluated. The results suggest that if only the correlation between SPI and SMA is considered, the maximum agreement is usually obtained with a zero lead time (almost 80% of the cases), with SPI-3 representing the best option in about 40% of the grid cells at global scale. By also accounting for the benefits of a positive lead time, short accumulation periods tend to be favored, with SPI-1 being the optimal choice in about one-half of the cases, and 10–20 days of lead time in more than 90% of the grid cells is achieved without any significant reduction in either correlation or skill in drought extreme detection.

Open access
Jordan Clark
and
Charles E. Konrad

Abstract

Wet-bulb globe temperature (WBGT) is used to assess environmental heat stress and accounts for the influences of air temperature, humidity, wind speed, and radiation on heat stress. Measurements of WBGT are highly sensitive to slight changes in environmental conditions and can vary several degrees Celsius across small distances (tens to hundreds of meters). Relative to observations with an International Organization for Standardization (ISO)-compliant WBGT meter, this work assesses the accuracy of WBGT measurements made with a popular handheld meter (the Kestrel 5400 Heat Stress Tracker) and WBGT estimates. Measurements were made during the summers of 2019–21 in a variety of suburban and urban environments in North Carolina, including three high school campuses. WBGT can be estimated from standard weather station variables, and many of these stations report cloud cover in lieu of solar radiation. Therefore, this work also evaluates the accuracy of clear-sky radiation estimates and adjustments to those estimates based on cloud cover. WBGT estimated with the method from Liljegren et al. from a weather station were on average 0.2°C warmer than Observed WBGT, while the Kestrel 5400 WBGT was 0.7°C warmer. Large variations in WBGT were observed across surfaces and shade conditions, with differences of 0.9°C (0.3°–1.4°C) between a tennis court and a neighboring grass field. The method for estimating clear-sky radiation in Ryan and Stolzenbach was most accurate and the clear-sky radiation modified by percentage cloud cover was found to be within 75 W m−2of observations on average.

Significance Statement

Wet-bulb globe temperature (WBGT) is a heat stress index that accounts for the effects of air temperature, humidity, wind, and radiation on humans. However, WBGT is not routinely measured at weather stations. This work demonstrated the accuracy of estimating WBGT with methods from , finding it to be more accurate than measurements from a popular handheld meter, the Kestrel 5400 Heat Stress Tracker. Variations in WBGT that result in different danger levels were found between measurements over a tennis court and a neighboring grass field, and between sun and shade conditions. Understanding the magnitude of these differences and the biases with WBGT estimates and measurements can inform the planning of outdoor activity to robustly safeguard health.

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Ruth Mendez-Rivas
,
Maycol Mena Palacios
, and
Reiner Palomino Lemus

Abstract

We investigated nine indices of spatiotemporal extreme precipitation events over Nicaragua during 2001–16, from GPCC, Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and IMERG, and their correlation with teleconnection patterns. The main objectives were to evaluate the variability of extreme precipitation events, to know the performance of IMERG and CHIRPS in the characterization of these extreme events, using GPCC and four rain gauges as references, and finally to determine the teleconnection patterns that have the highest correlation with these indices. The spatial coverage of the area with the highest number of consecutive days with daily precipitation less than 1 mm corresponds to the Pacific region, with annual mean values of up to 120 continuous days. Some extreme precipitation event indices (RR, RX1day, and RX5day) show a decreasing trend, suggesting that the study area has been experiencing a reduction of extreme precipitation indices in terms of intensities and duration throughout the study period. In addition, it was observed that CHIRPS shows a better fit when dealing with precipitation events that do not exceed certain thresholds and IMERG improves when describing intense precipitation event patterns. We found that the tropical Pacific SST EOF (EOFPAC), Niño-3.4, Pacific warm pool region (PACWARM), and SOI have a greater influence on extreme precipitation events, these results suggest that they are being controlled by ENSO episodes, providing a better understanding of the climate configuration, as a prediction and forecasting potential, useful for agriculture, land use, and risk management.

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Fang-Ching Chien
,
Chun-Wei Chang
,
Jen-Hsin Teng
, and
Jing-Shan Hong

Abstract

This paper investigates a wind speed oscillation event that occurred near the coastline of central Taiwan in the afternoon of 17 February 2018, using data from observations and numerical simulations. The observed wind speeds at 100-m altitude displayed a fast-oscillating pattern of about 6 cycles between strong winds of approximately 21 m s−1 and weak winds of around 2 m s−1, with periods of about 10 min. The pressure anomalies fluctuated in antiphase with the wind speed anomalies. The synoptic analysis revealed the influence of a continental high pressure system, resulting in a cold-air outbreak over Taiwan. The cold north-northeasterly winds split into two branches upon encountering Taiwan’s topography, with ridging off the east coast and a lee trough off the west coast of Taiwan. Wind oscillations were detected in the low-level cold air offshore the west coast of Taiwan, depicted by wavelike structures in wind speeds, sea level pressure, and potential temperature. The perturbations were identified as Kelvin-Helmholtz billows characterized by regions of strong wind speeds, warm and dry air, sinking motions, and low pressure collocated with each other, while regions of weaker wind speeds, cooler and moister air, ascending motions, and high pressure were associated with each other. With terrain contributing to favorable conditions, the large vertical and horizontal wind shears resulted from the southward acceleration of low-level cold air and the northward movement of the lee trough played an important role in initiating the wind oscillations.

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Emilee Lachenmeier
,
Rezaul Mahmood
,
Chris Phillips
,
Udaysankar Nair
,
Eric Rappin
,
Roger A. Pielke Sr.
,
William Brown
,
Steve Oncley
,
Joshua Wurman
,
Karen Kosiba
,
Aaron Kaulfus
,
Joseph Santanello Jr.
,
Edward Kim
,
Patricia Lawston-Parker
,
Michael Hayes
, and
Trenton E. Franz

Abstract

Modification of grasslands into irrigated and nonirrigated agriculture in the Great Plains resulted in significant impacts on weather and climate. However, there has been lack of observational data–based studies solely focused on impacts of irrigation on the PBL and convective conditions. The Great Plains Irrigation Experiment (GRAINEX), conducted during the 2018 growing season, collected data over irrigated and nonirrigated land uses over Nebraska to understand these impacts. Specifically, the objective was to determine whether the impacts of irrigation are sustained throughout the growing season. The data analyzed include latent and sensible heat flux, air temperature, dewpoint temperature, equivalent temperature (moist enthalpy), PBL height, lifting condensation level (LCL), level of free convection (LFC), and PBL mixing ratio. Results show increased partitioning of energy into latent heat relative to sensible heat over irrigated areas while average maximum air temperature was decreased and dewpoint temperature was increased from the early to peak growing season. Radiosonde data suggest reduced planetary boundary layer (PBL) heights at all launch sites from the early to peak growing season. However, reduction of PBL height was much greater over irrigated areas than over nonirrigated croplands. Relative to the early growing period, LCL and LFC heights were also lower during the peak growing period over irrigated areas. Results note, for the first time, that the impacts of irrigation on PBL evolution and convective environment can be sustained throughout the growing season and regardless of background atmospheric conditions. These are important findings and applicable to other irrigated areas in the world.

Significance Statement

To meet the ever-increasing demand for food, many regions of the world have adopted widespread irrigation. The High Plains Aquifer (HPA) region, located within the Great Plains of the United States, is one of the most extensively irrigated regions. In this study, for the first time, we have conducted a detailed irrigation-focused land surface and atmospheric data collection campaign to determine irrigation impacts on the atmosphere. This research demonstrates that irrigation significantly alters lower atmospheric characteristics and creates favorable cloud and convection development conditions during the growing season. The results clearly show first-order impacts of irrigation on regional weather and climate and hence warrant further attention so that we can minimize negative impacts and achieve sustainable irrigation.

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